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Utility of ChatGPT in Clinical Practice

Utility of ChatGPT in Clinical Practice

The purpose of this viewpoint is to provide an overview of the recent advances in Chat GPT in clinical practice (Multimedia Appendix 1 [5-16]), to explore the future direction of Chat GPT in clinical practice, to highlight the risks and challenges of its use in clinical practice, and to propose appropriate mitigation strategies. Although Chat GPT has demonstrated promising prospects in clinical practice, further research is needed to refine and improve its capabilities.

Jialin Liu, Changyu Wang, Siru Liu

J Med Internet Res 2023;25:e48568

Durability of the Effectiveness of Heterologous COVID-19 Vaccine Regimens in Thailand: Retrospective Cohort Study Using National Registration Data

Durability of the Effectiveness of Heterologous COVID-19 Vaccine Regimens in Thailand: Retrospective Cohort Study Using National Registration Data

In Thailand, health care workers were prioritized at the start of the campaign, and as it would be expected that health care workers might have different risks of developing severe COVID-19, this may have led to overestimates or underestimates of VE of the SV-AZ regimen in our study [46]. Similarly, we could not control for the comorbidities that increase the risk of developing severe COVID-19, such as diabetes and immunocompromising conditions.

Ponlagrit Kumwichar, Chittawan Poonsiri, Siobhan Botwright, Natchalaikorn Sirichumroonwit, Bootsakorn Loharjun, Supharerk Thawillarp, Nontawit Cheewaruangroj, Amorn Chokchaisiripakdee, Yot Teerawattananon, Virasakdi Chongsuvivatwong

JMIR Public Health Surveill 2024;10:e48255

Secondary Use of Clinical Data in Data-Gathering, Non-Interventional Research or Learning Activities: Definition, Types, and a Framework for Risk Assessment

Secondary Use of Clinical Data in Data-Gathering, Non-Interventional Research or Learning Activities: Definition, Types, and a Framework for Risk Assessment

Most importantly, patients can contribute their clinical data to research or learning activities without being exposed to immediate physical risks [5]. However, the secondary use of clinical data in research or learning activities entails data-associated risks that require further investigation.

Martin Jungkunz, Anja Köngeter, Katja Mehlis, Eva C Winkler, Christoph Schickhardt

J Med Internet Res 2021;23(6):e26631

A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study

A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study

Using the Shapley value framework, the model assesses risks at both the cohort and at the individual level, guiding the choice of therapeutic interventions. Data-driven approaches to building predictive risk models are becoming important in clinical applications as prescriptive analytics and targeted personalized therapy become more readily available [28,42].

Devika Subramanian, Rona Sonabend, Ila Singh

JMIR Diabetes 2024;9:e53338

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online

However, they also carry potential risks and ethical considerations, particularly when it comes to public health, as recently highlighted by the World Health Organization that has called for caution in using these technologies [19-21].

Amir Reza Ashraf, Tim Ken Mackey, András Fittler

JMIR Public Health Surveill 2024;10:e53086