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Consideration of Cybersecurity Risks in the Benefit-Risk Analysis of Medical Devices: Scoping Review

Oscar Freyer, Fatemeh Jahed, Max Ostermann, Christian Rosenzweig, Pascal Werner, Stephen Gilbert

J Med Internet Res 2024;26:e65528

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Holistic Human-Serving Digitization of Health Care Needs Integrated Automated System-Level Assessment Tools

Cindy Welzel, Fabienne Cotte, Magdalena Wekenborg, Baptiste Vasey, Peter McCulloch, Stephen Gilbert

J Med Internet Res 2023;25:e50158

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Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study

A study by Gilbert et al [68] comparing urgency advice (ie, triage) from 7 multicondition symptom checker apps and 7 general practitioners to gold-standard vignettes found that the condition suggested first matched the gold standard (ie, M1 accuracy) for 71% of general practitioners and 26% of apps; when broadening to the condition suggested in the top 5 (ie, M5 accuracy), the accuracy of general practitioners rose to 83% and apps to 41%.

Kimberly Peven, Aidan P Wickham, Octavia Wilks, Yusuf C Kaplan, Andrei Marhol, Saddif Ahmed, Ryan Bamford, Adam C Cunningham, Carley Prentice, András Meczner, Matthew Fenech, Stephen Gilbert, Anna Klepchukova, Sonia Ponzo, Liudmila Zhaunova

JMIR Mhealth Uhealth 2023;11:e46718

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Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies

Societal level feedback loops in regulation (figure concept developed by Stephen Gilbert, figure graphic design by Andrew Berry). Balancing goals for optimal regulation of medical devices. AIe MDs:artificial intelligence–enabled medical devices; DHT: digital health technologies. The 2017 EU Regulations for medical devices (MDR) governs the area of AIe MD and medical device DHT and sets out detailed requirements and obligations for product developers [14].

Stephen Gilbert, Stuart Anderson, Martin Daumer, Phoebe Li, Tom Melvin, Robin Williams

J Med Internet Res 2023;25:e43682

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Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study

Applying the definition from Gilbert et al [31], we will assess the accuracy and comprehensiveness of the top-1, top-3, and top-5 suggested conditions by the prototype DDSS in comparison to the gold standard differential diagnoses. Data analysis will also be carried out as presented by Gilbert. In short, descriptive tests and statistics appropriate for categorical data will be utilized to compare condition suggestion accuracy.

Marcel Schmude, Nahya Salim, Hila Azadzoy, Mustafa Bane, Elizabeth Millen, Lisa O’Donnell, Philipp Bode, Ewelina Türk, Ria Vaidya, Stephen Gilbert