A new viewpoint urges hospitals to pair emergency‑department AI with transparency, bias checks, and human oversight.
Artificial intelligence (AI)—especially large language models (LLMs) like ChatGPT—is moving from pilot projects to everyday support in emergency medicine. A peer‑reviewed viewpoint in JMIR Medical Informatics outlines where AI already helps and where guardrails are urgently needed to keep patients safe.
Where AI is helping today
Hospitals are testing tools that predict who needs admission, speed triage in crowded waiting rooms, and forecast bed availability and staffing needs. Algorithms can rapidly read scans for fractures or head injuries, flag possible heart attacks from ECGs, and help spot early sepsis—sometimes cutting time to antibiotics. Unlike traditional rule‑based decision aids, modern AI learns patterns directly from data, which can surface subtle risks clinicians might miss.
Why experts urge caution
LLMs can “hallucinate”—confidently giving wrong answers—and they’re least reliable on rare or atypical cases that emergency clinicians see every day. Bias is another risk: models trained on narrow datasets may work well for some groups and poorly for others. One dermatology example cited in the paper showed 17% diagnostic accuracy on very dark skin versus 69.9% on lighter skin tones, underscoring the stakes for equity in care. The authors recommend diversified training data, ongoing audits, and techniques that let models signal uncertainty so clinicians know when not to trust the output.
Make AI explain itself—then keep humans in charge
Because “black‑box” predictions are hard to trust in life‑or‑death settings, the authors call for explainable AI (XAI)—methods that show which factors drove a recommendation—and for clear, rapid “override” pathways so clinicians can overrule the algorithm at the bedside. Hospitals should also document AI‑influenced decisions and set up oversight committees to review performance and near‑misses.
Rules and responsibility are still catching up
Regulation is evolving unevenly: the EU’s AI Act applies a risk‑based framework; the U.S. leans on the FDA’s rules for software as a medical device; many countries have no binding health‑AI rules at all (the paper estimates only ~15% do). Liability when AI steers a bad outcome remains murky, strengthening the case for rigorous validation before clinical deployment.
Main takeaway
AI can ease bottlenecks and support faster, more accurate emergency care—but it’s not a replacement for clinical judgment. Hospitals adopting these tools should insist on transparency, fairness checks, and continuous monitoring, and train staff to use AI critically. Done right, AI becomes a helpful second set of eyes in the ER—powerful, but always supervised.
Source: Amiot F, Potier B. “Artificial Intelligence (AI) and Emergency Medicine: Balancing Opportunities and Challenges.” JMIR Medical Informatics. Published Aug 13, 2025.
Editor’s note: This article is for information only and is not a substitute for professional medical advice.