Managing Editor
@ShahidNShah
@ShahidNShah
Rethinking AI Workflows: Guidelines for Scientific Evaluation in Digital Health Companies
Artificial intelligence (AI) is revolutionizing digital health, driving innovation in care delivery and operational efficiency. Despite its potential, many AI systems fail to meet real-world expectations due to limited evaluation practices that focus narrowly on short-term metrics like efficiency and technical accuracy.
Medigy Insights
Explainable AI models using electronic health record data can accurately predict which patients are at high risk of hospital readmission, helping clinicians target interventions where they’re most needed. By providing transparent reasoning for predictions, these models can build clinician trust and support better care planning to reduce avoidable readmissions.
Continue reading at ai.jmir.org
Make faster decisions with community advice
- Assessing the Quality of AI Responses to Patient Concerns About Axial Spondyloarthritis: Delphi-Based Evaluation
- Digital Hypertension in 2024–2025: Emerging Evidence and Future Directions
- Physical Examination Identification in Medical Education Videos: Zero-Shot Multimodal AI With Temporal Sequence Optimization Study
- The Next Frontier of Remote Patient Monitoring: Hospital at Home
- Bridging The Digital Health Divide: Digital Endowment, Informal Social Participation, And Health Inequality Among Older Adults
Next Article
-
Physical Examination Identification in Medical Education Videos: Zero-Shot Multimodal AI With Temporal Sequence Optimization Study
Objective structured clinical examinations (OSCEs) are widely used for assessing medical student competency, but their evaluation is resource-intensive, requiring trained evaluators to review …
