Exploration of Digital Health Technologies

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Protocol

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam
Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and Zika are increasingl [...] Read more.

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

Praveen Kumar ... Andrew W. Taylor-Robinson

Published: November 01, 2023 Explor Digit Health Technol. 2023;1:17–27
DOI: https://doi.org/10.37349/edht.2023.00004

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Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

Open Access

Perspective

Turbulence at Twitter with leadership change: implications for health research and science communication
Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists [...] Read more.

Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication.

Ronan Lordan, Hari Prasad Devkota

Published: November 01, 2023 Explor Digit Health Technol. 2023;1:4–10
DOI: https://doi.org/10.37349/edht.2023.00002
This article belongs to the special issue Social Media Applications in Biomedical Research

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Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication.

Open Access

Letter to the Editor

Harnessing the untapped potential of digital twin technology in digital public health interventions
Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cyberneti [...] Read more.

Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology.

Salman Khan ... ArunSundar MohanaSundaram

Published: November 01, 2023 Explor Digit Health Technol. 2023;1:11–16
DOI: https://doi.org/10.37349/edht.2023.00003
This article belongs to the special issue Social Media Applications in Biomedical Research

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Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology.

Open Access

Perspective

Science communication on X (formerly Twitter): A picture is worth a thousand characters?
X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public [...] Read more.

X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X.

Himel Mondal ... Harald Willschke

Published: November 28, 2023 Explor Digit Health Technol. 2023;1:28–34
DOI: https://doi.org/10.37349/edht.2023.00005

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X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X.

Open Access

Letter to the Editor

Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references
The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of c [...] Read more.

The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.

Catherine L. Clelland ... James D. Clelland

Published: January 10, 2024 Explor Digit Health Technol. 2024;2:1–6
DOI: https://doi.org/10.37349/edht.2024.00006

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The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.

Open Access

Editorial

Exploration of Digital Health Technologies

Atanas G. Atanasov

Published: November 01, 2023 Explor Digit Health Technol. 2023;1:1–3
DOI: https://doi.org/10.37349/edht.2023.00001

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Open Access

Original Article

The power of #physiotherapy: a social media hashtag investigation on X (formerly Twitter)
Aim: The social media platform X, formerly known as Twitter, has emerged as a significant hub for healthcare-related conversations and sharing information. This study aims to investigate the impa [...] Read more.

Aim: The social media platform X, formerly known as Twitter, has emerged as a significant hub for healthcare-related conversations and sharing information. This study aims to investigate the impact and reach of the #physiotherapy hashtag on the X platform. Methods: We collected and analyzed tweets containing the hashtag #physiotherapy posted between September 1, 2022, and September 1, 2023. Data was retrieved from X using the Fedica analytics platform on October 26, 2023. The data were analyzed and expressed in number and percentage and categorical data were tested by chi-square test. Results: Over the course of one year, a total of 57,788 tweets were shared using #physiotherapy by 21,244 users, generating a remarkable 108,743,911 impressions. On average, there were 6 tweets posted per day (with a range from 3 to 9). Among the users, the majority (42%) had between 100 and 1000 followers, while 31.6% had fewer than 100 followers. The top three countries contributing to #physiotherapy tweets were the UK (29.9%), India (23.75%), and the USA (11.85%). An analysis of sentiment revealed that 84% of the tweets had a neutral tone, while 9% were positive and 7% were negative (P < 0.0001). Conclusions: The examination of tweets related to #physiotherapy unveiled a vibrant global dialogue, with active engagement from diverse backgrounds. Notably, contributions from the UK, India, and the USA were prominent.

Himel Mondal ... Atanas G. Atanasov

Published: June 24, 2024 Explor Digit Health Technol. 2024;2:135–144
DOI: https://doi.org/10.37349/edht.2024.00016
This article belongs to the special issue Social Media Applications in Biomedical Research

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Aim: The social media platform X, formerly known as Twitter, has emerged as a significant hub for healthcare-related conversations and sharing information. This study aims to investigate the impact and reach of the #physiotherapy hashtag on the X platform. Methods: We collected and analyzed tweets containing the hashtag #physiotherapy posted between September 1, 2022, and September 1, 2023. Data was retrieved from X using the Fedica analytics platform on October 26, 2023. The data were analyzed and expressed in number and percentage and categorical data were tested by chi-square test. Results: Over the course of one year, a total of 57,788 tweets were shared using #physiotherapy by 21,244 users, generating a remarkable 108,743,911 impressions. On average, there were 6 tweets posted per day (with a range from 3 to 9). Among the users, the majority (42%) had between 100 and 1000 followers, while 31.6% had fewer than 100 followers. The top three countries contributing to #physiotherapy tweets were the UK (29.9%), India (23.75%), and the USA (11.85%). An analysis of sentiment revealed that 84% of the tweets had a neutral tone, while 9% were positive and 7% were negative (P < 0.0001). Conclusions: The examination of tweets related to #physiotherapy unveiled a vibrant global dialogue, with active engagement from diverse backgrounds. Notably, contributions from the UK, India, and the USA were prominent.

Open Access

Original Article

Tracing participants for longitudinal environmental health research using social networking sites: a pilot study
Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations i [...] Read more.

Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations including participant attrition, and the resource implications associated with cohort recruitment and follow-up. Attrition compromises the integrity of research by threatening both the internal and external validity of empirical results, weakening the accuracy of statistical inferences and the generalizability of findings. This pilot study aimed to trace participants from a historical cohort study, the Hamilton Child Cohort Study (HCC) (n = 3,202), (1976–1986, 2003–2008) which was originally designed to examine the relative contribution of indoor and outdoor exposure to air pollution on respiratory health. Methods: Original participants were traced through social networking sites (SNS) by leveraging personal identifying data (name, age, sex, educational affiliation, and geographical locations) from the HCC entered into SNS search engines. Results: Of the original cohort (n = 3,166), 21% (n = 665) were identified as having social media presence (SMP) on a single social media platform, with 15% (n = 479) found on Facebook, 6% (n = 185) on LinkedIn, < 1% (n = 9) on Instagram, and n = 1 participant on Twitter. However, 68% (n = 2,168) of the cohort were associated with multiple SNS with the same features (matching names, ages, and locations), making conclusive identification challenging. The remaining 11% (n = 334) of the cohort had no SMP (NSMP). Statistical differences in sample characteristics of each cohort were analyzed using the Pearson chi-square test. Significant differences between the SMP and NSMP cohorts were found in relation to sex (p < 0.001), and childhood neighborhood of residence (p < 0.05). Conclusions: This study underscores social media’s potential for tracing participants in longitudinal studies while advising a multi-faceted approach to overcome inherent limitations and biases. A full-scale study is necessary to determine whether utilizing SNS to trace participants for longitudinal research is an effective tool for re-engaging research participants lost to attrition.

Rogih Riad Andrawes ... Caroline Barakat

Published: June 25, 2024 Explor Digit Health Technol. 2024;2:145–156
DOI: https://doi.org/10.37349/edht.2024.00017
This article belongs to the special issue Social Media Applications in Biomedical Research

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Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations including participant attrition, and the resource implications associated with cohort recruitment and follow-up. Attrition compromises the integrity of research by threatening both the internal and external validity of empirical results, weakening the accuracy of statistical inferences and the generalizability of findings. This pilot study aimed to trace participants from a historical cohort study, the Hamilton Child Cohort Study (HCC) (n = 3,202), (1976–1986, 2003–2008) which was originally designed to examine the relative contribution of indoor and outdoor exposure to air pollution on respiratory health. Methods: Original participants were traced through social networking sites (SNS) by leveraging personal identifying data (name, age, sex, educational affiliation, and geographical locations) from the HCC entered into SNS search engines. Results: Of the original cohort (n = 3,166), 21% (n = 665) were identified as having social media presence (SMP) on a single social media platform, with 15% (n = 479) found on Facebook, 6% (n = 185) on LinkedIn, < 1% (n = 9) on Instagram, and n = 1 participant on Twitter. However, 68% (n = 2,168) of the cohort were associated with multiple SNS with the same features (matching names, ages, and locations), making conclusive identification challenging. The remaining 11% (n = 334) of the cohort had no SMP (NSMP). Statistical differences in sample characteristics of each cohort were analyzed using the Pearson chi-square test. Significant differences between the SMP and NSMP cohorts were found in relation to sex (p < 0.001), and childhood neighborhood of residence (p < 0.05). Conclusions: This study underscores social media’s potential for tracing participants in longitudinal studies while advising a multi-faceted approach to overcome inherent limitations and biases. A full-scale study is necessary to determine whether utilizing SNS to trace participants for longitudinal research is an effective tool for re-engaging research participants lost to attrition.

Open Access

Original Article

HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicine
Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outc [...] Read more.

Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.

Neha Deo ... Rahul Kashyap

Published: June 30, 2024 Explor Digit Health Technol. 2024;2:157–166
DOI: https://doi.org/10.37349/edht.2024.00018

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Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.

Open Access

Review

Social media applications in biomedical research
Information and communication technologies (ICTs) have transformed global connectivity, offering significant support to underserved populations and small businesses in developing nations. The integr [...] Read more.

Information and communication technologies (ICTs) have transformed global connectivity, offering significant support to underserved populations and small businesses in developing nations. The integration of social media into the ICT landscape has further revolutionized communication and information sharing worldwide. However, despite its widespread adoption, the precise impact of social media on biomedical research remains uncertain. This manuscript seeks to examine the multifaceted roles of social media in healthcare, focusing on its applications in patient care, professional networking, education, organizational promotion, and public health programs. Additionally, it investigates social media’s significance in research, particularly its potential for data collection and analysis. A comprehensive literature review was undertaken to consolidate existing knowledge on social media’s utilization in healthcare and research. Various platforms, including social networking sites and academic networking sites, were assessed, along with their respective applications and consequences. Social media platforms have become essential tools in healthcare, facilitating professional networking, patient education, organizational promotion, and public health initiatives. In the realm of research, social media provides extensive opportunities for data collection, analysis, and collaboration, although challenges persist regarding privacy, data accuracy, and ethical considerations. The pervasive influence of social media in healthcare and research highlights its potential to enhance communication, engagement, and knowledge dissemination. However, careful adherence to ethical guidelines and privacy concerns is essential to maximize its benefits while minimizing risks. As social media continues to evolve, its role in shaping biomedical research and healthcare practices is anticipated to grow, necessitating ongoing exploration and adaptation by stakeholders.

Md Sadique Hussain, Devesh Tewari

Published: July 04, 2024 Explor Digit Health Technol. 2024;2:167–182
DOI: https://doi.org/10.37349/edht.2024.00019
This article belongs to the special issue Social Media Applications in Biomedical Research

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Information and communication technologies (ICTs) have transformed global connectivity, offering significant support to underserved populations and small businesses in developing nations. The integration of social media into the ICT landscape has further revolutionized communication and information sharing worldwide. However, despite its widespread adoption, the precise impact of social media on biomedical research remains uncertain. This manuscript seeks to examine the multifaceted roles of social media in healthcare, focusing on its applications in patient care, professional networking, education, organizational promotion, and public health programs. Additionally, it investigates social media’s significance in research, particularly its potential for data collection and analysis. A comprehensive literature review was undertaken to consolidate existing knowledge on social media’s utilization in healthcare and research. Various platforms, including social networking sites and academic networking sites, were assessed, along with their respective applications and consequences. Social media platforms have become essential tools in healthcare, facilitating professional networking, patient education, organizational promotion, and public health initiatives. In the realm of research, social media provides extensive opportunities for data collection, analysis, and collaboration, although challenges persist regarding privacy, data accuracy, and ethical considerations. The pervasive influence of social media in healthcare and research highlights its potential to enhance communication, engagement, and knowledge dissemination. However, careful adherence to ethical guidelines and privacy concerns is essential to maximize its benefits while minimizing risks. As social media continues to evolve, its role in shaping biomedical research and healthcare practices is anticipated to grow, necessitating ongoing exploration and adaptation by stakeholders.

Open Access

Perspective

Artificial intelligence facilitates clinical management of epithelial dysplasia in multiple organs
Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associat [...] Read more.

Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.

Xin-Jia Cai

Published: July 08, 2024 Explor Digit Health Technol. 2024;2:183–187
DOI: https://doi.org/10.37349/edht.2024.00020
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer

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Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.

Open Access

Review

Digital twin technology training and research in health higher education: a review
Healthcare strives to ensure overall physical, mental, and emotional well-being for individuals while managing limited resources efficiently. Digital technologies can offer cost reduction, improved [...] Read more.

Healthcare strives to ensure overall physical, mental, and emotional well-being for individuals while managing limited resources efficiently. Digital technologies can offer cost reduction, improved user experience, and expanded capacity. In addition, modern automation technologies, which were implemented in industrial control systems or industrial automation control systems, are essential for ensuring the availability of societies’ critical cyber-physical systems (CPSs) and the services they provide, such as healthcare. This narrative literature review produces information that can be applied when planning and implementing an interdisciplinary biomedical and health informatics (BMHI) master’s education focused on the challenges of digitalization in the health sector. The review results that virtual human twins (VHTs) are revolutionizing healthcare by addressing people’s complex medical problems with real-time monitoring and precision care while digital twin (DT) technology can make the hospital’s operational processes resilient and efficient. Thus, future BMHI education must address these technologies with a multidisciplinary approach, including computer science, information science, engineering, basic sciences, health sciences, socio-behavioral sciences, and ethical, legal, and policy aspects. Collected and cumulative data is essential for cognitive DTs. A prerequisite for this data is information sharing between different CPSs. Better information sharing and the development of scalable cognitive DTs and VHTs, the provision of critical services, quality, and cost-effectiveness, as well as health, safety, and resilience, will improve. Similarities between peoples’ health information exchange and information needed for ensuring the resilience of CPSs exist. Since humans are in many ways more complex than CPSs, security engineers have a lot to learn from VHTs in maintaining the resilience of CPSs. Cross-sectoral research and cooperation with different disciplines are essential for the progress of both human health and the resilience of CPSs. Along with interdisciplinary research cooperation, educational cooperation should also be intensified.

Jyri Rajamäki

Published: August 05, 2024 Explor Digit Health Technol. 2024;2:188–201
DOI: https://doi.org/10.37349/edht.2024.00021
This article belongs to the special issue Data-informed Decision Making in Healthcare

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Healthcare strives to ensure overall physical, mental, and emotional well-being for individuals while managing limited resources efficiently. Digital technologies can offer cost reduction, improved user experience, and expanded capacity. In addition, modern automation technologies, which were implemented in industrial control systems or industrial automation control systems, are essential for ensuring the availability of societies’ critical cyber-physical systems (CPSs) and the services they provide, such as healthcare. This narrative literature review produces information that can be applied when planning and implementing an interdisciplinary biomedical and health informatics (BMHI) master’s education focused on the challenges of digitalization in the health sector. The review results that virtual human twins (VHTs) are revolutionizing healthcare by addressing people’s complex medical problems with real-time monitoring and precision care while digital twin (DT) technology can make the hospital’s operational processes resilient and efficient. Thus, future BMHI education must address these technologies with a multidisciplinary approach, including computer science, information science, engineering, basic sciences, health sciences, socio-behavioral sciences, and ethical, legal, and policy aspects. Collected and cumulative data is essential for cognitive DTs. A prerequisite for this data is information sharing between different CPSs. Better information sharing and the development of scalable cognitive DTs and VHTs, the provision of critical services, quality, and cost-effectiveness, as well as health, safety, and resilience, will improve. Similarities between peoples’ health information exchange and information needed for ensuring the resilience of CPSs exist. Since humans are in many ways more complex than CPSs, security engineers have a lot to learn from VHTs in maintaining the resilience of CPSs. Cross-sectoral research and cooperation with different disciplines are essential for the progress of both human health and the resilience of CPSs. Along with interdisciplinary research cooperation, educational cooperation should also be intensified.

Open Access

Original Article

Developing a multi-variate prediction model for COVID-19 from crowd-sourced respiratory voice data
Aim: COVID-19 has affected more than 223 countries worldwide and in the post-COVID era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. This [...] Read more.

Aim: COVID-19 has affected more than 223 countries worldwide and in the post-COVID era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. This study focuses on the analysis of voice features and machine learning models in the automatic detection of COVID-19. Methods: We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4,352 participants via a COVID-19 Sounds app. Voice features including Mel-spectrograms and Mel-frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) Encoder features are extracted. Based on the voice data, we develop deep learning classification models to detect COVID-19 cases. These models include long short-term memory (LSTM), CNN and Hidden-Unit BERT (HuBERT). Results: We compare their predictive power to baseline machine learning models. HuBERT achieves the highest accuracy of 86% and the highest AUC of 0.93. Conclusions: The results achieved with the proposed models suggest promising results in COVID-19 diagnosis from voice recordings when compared to the results obtained from the state-of-the-art.

Yuyang Yan ... Visara Urovi

Published: August 11, 2024 Explor Digit Health Technol. 2024;2:202–217
DOI: https://doi.org/10.37349/edht.2024.00022
This article belongs to the special issue Data-informed Decision Making in Healthcare

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Aim: COVID-19 has affected more than 223 countries worldwide and in the post-COVID era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. This study focuses on the analysis of voice features and machine learning models in the automatic detection of COVID-19. Methods: We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4,352 participants via a COVID-19 Sounds app. Voice features including Mel-spectrograms and Mel-frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) Encoder features are extracted. Based on the voice data, we develop deep learning classification models to detect COVID-19 cases. These models include long short-term memory (LSTM), CNN and Hidden-Unit BERT (HuBERT). Results: We compare their predictive power to baseline machine learning models. HuBERT achieves the highest accuracy of 86% and the highest AUC of 0.93. Conclusions: The results achieved with the proposed models suggest promising results in COVID-19 diagnosis from voice recordings when compared to the results obtained from the state-of-the-art.

Open Access

Original Article

An introduction to Self-Aware Deep Learning for medical imaging and diagnosis
Aim: This study represents preliminary research for testing the effectiveness of the Self-Aware Deep Learning (SAL) methodology in the context of medical diagnostics using various types of attrib [...] Read more.

Aim: This study represents preliminary research for testing the effectiveness of the Self-Aware Deep Learning (SAL) methodology in the context of medical diagnostics using various types of attributes. By enhancing traditional AI models with self-aware capabilities, this approach seeks to improve diagnostic accuracy and patient outcomes in medical settings. Methods: This research discusses an introduction of SAL methodology into the medical field. SAL incorporates continuous self-assessment, allowing the AI to adjust its parameters and structure autonomously in response to changing inputs and performance metrics. The methodology is applied to medical diagnostics, utilizing real medical datasets available in the public domain. These datasets encompass a partial range of medical conditions and diagnostic scenarios, providing an initial test background for a preliminary evaluation of the effectiveness of SAL in a real-world context. Results: The study shows encouraging results in enhancing diagnostic accuracy and patient outcomes. Through continuous assessment and autonomous adjustments of its own neural network architecture, self-aware AI systems show improvements in adaptability, in the classification of real datasets and diagnostic process. Additional experiments on expanded data sets are necessary for validating these preliminary results. Conclusions: Tests on real data show that Self-Aware Deep Neural Networks present promising potential for improving medical diagnostic capabilities. They offer advantages such as enhanced adaptability to varying data qualities, improved error detection, efficient resource allocation, and increased transparency in AI-assisted diagnoses. However, considering the limited size of the test data set used in this research, further validation is required through additional experiments on larger data sets.

Paolo Dell’Aversana

Published: August 15, 2024 Explor Digit Health Technol. 2024;2:218–234
DOI: https://doi.org/10.37349/edht.2024.00023
This article belongs to the special issue Deep Learning Methods and Applications for Biomedical Imaging

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Aim: This study represents preliminary research for testing the effectiveness of the Self-Aware Deep Learning (SAL) methodology in the context of medical diagnostics using various types of attributes. By enhancing traditional AI models with self-aware capabilities, this approach seeks to improve diagnostic accuracy and patient outcomes in medical settings. Methods: This research discusses an introduction of SAL methodology into the medical field. SAL incorporates continuous self-assessment, allowing the AI to adjust its parameters and structure autonomously in response to changing inputs and performance metrics. The methodology is applied to medical diagnostics, utilizing real medical datasets available in the public domain. These datasets encompass a partial range of medical conditions and diagnostic scenarios, providing an initial test background for a preliminary evaluation of the effectiveness of SAL in a real-world context. Results: The study shows encouraging results in enhancing diagnostic accuracy and patient outcomes. Through continuous assessment and autonomous adjustments of its own neural network architecture, self-aware AI systems show improvements in adaptability, in the classification of real datasets and diagnostic process. Additional experiments on expanded data sets are necessary for validating these preliminary results. Conclusions: Tests on real data show that Self-Aware Deep Neural Networks present promising potential for improving medical diagnostic capabilities. They offer advantages such as enhanced adaptability to varying data qualities, improved error detection, efficient resource allocation, and increased transparency in AI-assisted diagnoses. However, considering the limited size of the test data set used in this research, further validation is required through additional experiments on larger data sets.

Open Access

Systematic Review

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults
Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data scienc [...] Read more.

Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

Ard Hendriks ... Sil Aarts

Published: April 12, 2024 Explor Digit Health Technol. 2024;2:67–85
DOI: https://doi.org/10.37349/edht.2024.00012
This article belongs to the special issue Data-informed Decision Making in Healthcare

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Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

Open Access

Original Article

Digital health and mobile health: a bibliometric analysis of the 100 most cited papers and their contributing authors
Aim: This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new rese [...] Read more.

Aim: This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest. Methods: On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map. Results: The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19). Conclusions: The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health.

Andy Wai Kan Yeung ... Atanas G. Atanasov

Published: April 22, 2024 Explor Digit Health Technol. 2024;2:86–100
DOI: https://doi.org/10.37349/edht.2024.00013

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Aim: This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest. Methods: On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map. Results: The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19). Conclusions: The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health.

Open Access

Systematic Review

Do you need a blockchain in healthcare data sharing? A tertiary review
Background: This study addresses the complexities of utilizing blockchain technology in healthcare, aiming to provide a decision-making tool for healthcare professionals and policymakers evaluati [...] Read more.

Background: This study addresses the complexities of utilizing blockchain technology in healthcare, aiming to provide a decision-making tool for healthcare professionals and policymakers evaluating blockchain’s suitability for healthcare data sharing applications. Methods: A tertiary review was conducted on existing systematic literature reviews concerning blockchain in the healthcare domain. Reviews that focused on data sharing were selected, and common key factors assessing blockchain’s suitability in healthcare were extracted. Results: Our review synthesized findings from 27 systematic literature reviews, which led to the development of a refined decision-making flowchart. This tool outlines criteria such as scalability, integrity/immutability, interoperability, transparency, patient involvement, cost, and public verifiability, essential for assessing the suitability of blockchain in healthcare data sharing. This flowchart was validated through multiple case studies from various healthcare domains, testing its utility in real-world scenarios. Discussion: Blockchain technology could significantly benefit healthcare data sharing, provided its application is carefully evaluated against tailored criteria for healthcare needs. The decision-making flowchart developed from this review offers a systematic approach to assist stakeholders in navigating the complexities of implementing blockchain technology in healthcare settings.

Kun Li ... Visara Urovi

Published: June 14, 2024 Explor Digit Health Technol. 2024;2:101–123
DOI: https://doi.org/10.37349/edht.2024.00014
This article belongs to the special issue Data-informed Decision Making in Healthcare

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Background: This study addresses the complexities of utilizing blockchain technology in healthcare, aiming to provide a decision-making tool for healthcare professionals and policymakers evaluating blockchain’s suitability for healthcare data sharing applications. Methods: A tertiary review was conducted on existing systematic literature reviews concerning blockchain in the healthcare domain. Reviews that focused on data sharing were selected, and common key factors assessing blockchain’s suitability in healthcare were extracted. Results: Our review synthesized findings from 27 systematic literature reviews, which led to the development of a refined decision-making flowchart. This tool outlines criteria such as scalability, integrity/immutability, interoperability, transparency, patient involvement, cost, and public verifiability, essential for assessing the suitability of blockchain in healthcare data sharing. This flowchart was validated through multiple case studies from various healthcare domains, testing its utility in real-world scenarios. Discussion: Blockchain technology could significantly benefit healthcare data sharing, provided its application is carefully evaluated against tailored criteria for healthcare needs. The decision-making flowchart developed from this review offers a systematic approach to assist stakeholders in navigating the complexities of implementing blockchain technology in healthcare settings.

Open Access

Mini Review

Interaction of electromagnetic fields with body-onboard devices
The aim of this contribution is to analyze and discuss the perturbations of body-onboard medical devices caused by electromagnetic field radiations. This involves their control via electromagnetic c [...] Read more.

The aim of this contribution is to analyze and discuss the perturbations of body-onboard medical devices caused by electromagnetic field radiations. This involves their control via electromagnetic compatibility analysis and their protection against such perturbations. The wearable, detachable, and embedded devices are first presented and their monitoring, control, forecasting, and stimulating functions are detailed. The interaction of these devices with field exposures comprising their wireless routines is then analyzed. The perturbations control of onboard devices is investigated through the mathematical solution of governing electromagnetic field equations and their appropriate protection strategies are deliberated. The involved investigations and analyses in the contribution are supported by a literature review.

Adel Razek

Published: June 17, 2024 Explor Digit Health Technol. 2024;2:124–134
DOI: https://doi.org/10.37349/edht.2024.00015
This article belongs to the special issue Biosensors for Bioactive Molecules

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The aim of this contribution is to analyze and discuss the perturbations of body-onboard medical devices caused by electromagnetic field radiations. This involves their control via electromagnetic compatibility analysis and their protection against such perturbations. The wearable, detachable, and embedded devices are first presented and their monitoring, control, forecasting, and stimulating functions are detailed. The interaction of these devices with field exposures comprising their wireless routines is then analyzed. The perturbations control of onboard devices is investigated through the mathematical solution of governing electromagnetic field equations and their appropriate protection strategies are deliberated. The involved investigations and analyses in the contribution are supported by a literature review.

Open Access

Review

Rise of the machines: trends and challenges of implementing AI in biomedical scientific writing
Artificial intelligence (AI) technology is advancing significantly, with many applications already in medicine, healthcare, and biomedical research. Among these fields, the area that AI is remarkabl [...] Read more.

Artificial intelligence (AI) technology is advancing significantly, with many applications already in medicine, healthcare, and biomedical research. Among these fields, the area that AI is remarkably reshaping is biomedical scientific writing. Thousands of AI-based tools can be applied at every step of the writing process, improving time effectiveness, and streamlining authors’ workflow. Out of this variety, choosing the best software for a particular task may pose a challenge. While ChatGPT receives the necessary attention, other AI software should be addressed. In this review, we draw attention to a broad spectrum of AI tools to provide users with a perspective on which steps of their work can be improved. Several medical journals developed policies toward the usage of AI in writing. Even though they refer to the same technology, they differ, leaving a substantially gray area prone to abuse. To address this issue, we comprehensively discuss common ambiguities regarding AI in biomedical scientific writing, such as plagiarism, copyrights, and the obligation of reporting its implementation. In addition, this article aims to raise awareness about misconduct due to insufficient detection, lack of reporting, and unethical practices revolving around AI that might threaten unaware authors and medical society. We provide advice for authors who wish to implement AI in their daily work, emphasizing the need for transparency and the obligation together with the responsibility to maintain biomedical research credibility in the age of artificially enhanced science.

Michal Fornalik ... Aleksandra Zielińska

Published: September 04, 2024 Explor Digit Health Technol. 2024;2:235–248
DOI: https://doi.org/10.37349/edht.2024.00024

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Artificial intelligence (AI) technology is advancing significantly, with many applications already in medicine, healthcare, and biomedical research. Among these fields, the area that AI is remarkably reshaping is biomedical scientific writing. Thousands of AI-based tools can be applied at every step of the writing process, improving time effectiveness, and streamlining authors’ workflow. Out of this variety, choosing the best software for a particular task may pose a challenge. While ChatGPT receives the necessary attention, other AI software should be addressed. In this review, we draw attention to a broad spectrum of AI tools to provide users with a perspective on which steps of their work can be improved. Several medical journals developed policies toward the usage of AI in writing. Even though they refer to the same technology, they differ, leaving a substantially gray area prone to abuse. To address this issue, we comprehensively discuss common ambiguities regarding AI in biomedical scientific writing, such as plagiarism, copyrights, and the obligation of reporting its implementation. In addition, this article aims to raise awareness about misconduct due to insufficient detection, lack of reporting, and unethical practices revolving around AI that might threaten unaware authors and medical society. We provide advice for authors who wish to implement AI in their daily work, emphasizing the need for transparency and the obligation together with the responsibility to maintain biomedical research credibility in the age of artificially enhanced science.

Open Access

Perspective

How to improve photographs with smartphones for oral telemedicine
Photographic images are an essential tool in oral medicine practice, even though their value is conditioned by their quality. Digital photography using smartphones (SPhs) has had many advances, nowa [...] Read more.

Photographic images are an essential tool in oral medicine practice, even though their value is conditioned by their quality. Digital photography using smartphones (SPhs) has had many advances, nowadays allowing the acquisition of high-quality pictures. Compared to professional cameras, it has advantages and disadvantages. The latter comprise photographs out of focus, poorly framed, and lighting problems due to shadows, artifacts, and color alterations, among other problems mainly mediated by the operator. Such defects can limit the proper interpretation of the image representing the patient’s condition. This perspective aims to describe the basic concepts of photography and the functional features of SPhs. This will allow programming those devices properly for oral telemedicine (OTM), understanding their limitations, and correcting errors for the photographs to be used effectively. We also include empirical solutions and illustrations showing that photography with SPhs could be easily executable by any health professional and even by the patients themselves.

Eduardo D. Piemonte ... Jerónimo P. Lazos

Published: September 13, 2024 Explor Digit Health Technol. 2024;2:249–258
DOI: https://doi.org/10.37349/edht.2024.00025
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer

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Photographic images are an essential tool in oral medicine practice, even though their value is conditioned by their quality. Digital photography using smartphones (SPhs) has had many advances, nowadays allowing the acquisition of high-quality pictures. Compared to professional cameras, it has advantages and disadvantages. The latter comprise photographs out of focus, poorly framed, and lighting problems due to shadows, artifacts, and color alterations, among other problems mainly mediated by the operator. Such defects can limit the proper interpretation of the image representing the patient’s condition. This perspective aims to describe the basic concepts of photography and the functional features of SPhs. This will allow programming those devices properly for oral telemedicine (OTM), understanding their limitations, and correcting errors for the photographs to be used effectively. We also include empirical solutions and illustrations showing that photography with SPhs could be easily executable by any health professional and even by the patients themselves.
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