Key idea
AI can help organize information, recognize patterns, automate documentation, and accelerate research. It cannot replace clinical accountability, informed consent, high-quality evidence, or human judgment.
AI-assisted diagnostics
Medical imaging, pathology, dermatology, and risk prediction are major areas of research. Useful systems are evaluated for accuracy, calibration, bias, workflow fit, and performance across different populations.
Wearables and remote monitoring
Consumer and clinical devices can collect heart rate, activity, sleep, temperature, rhythm, glucose, oxygen, and other signals. Data can reveal patterns, but false alarms, missing context, and measurement limits matter.
AI-supported drug discovery
Computational models can help identify targets, screen compounds, predict properties, and organize scientific literature. Laboratory validation, clinical trials, regulatory review, and post-market monitoring remain essential.
Virtual care and digital therapeutics
Telehealth expands access, while software-based interventions may support behavior change or condition management. Privacy, accessibility, reimbursement, and evidence quality influence value.
Questions every reader should ask
- What problem is the system designed to solve?
- Who tested it, on which populations, and against what standard?
- Who is responsible when it is wrong?
- What data does it collect, and who can access it?
- Does it improve outcomes, or only make a prediction?