AI Updates and Regressions
Description
We examine how clinician-built AI can safely support emergency care, where consumer tools fall short, and why planning, context, and evaluation matter more than model hype. We also share a patient-facing approach to unify records and recordings for safer, clearer answers.
• differences between consumer and medical‑grade AI, HIPAA and BAAs
• model regressions, sycophancy, and hallucinations
• context engineering and planned prompting for safety
• ambient clinical decision support at the bedside
• evaluations, benchmarks, and model selection
• medico‑legal uncertainty and state regulations
• education risks of over‑reliance on AI
• human oversight, prioritization, and tactile care
• patient empowerment via unified records and encounter recordings
• interoperability gaps and practical workarounds
United States
