Recent advances in generative artificial intelligence have introduced large language models (LLMs) into various medical fields, promising to enhance clinical documentation and decision support. Despite these benefits, the integration of these models into healthcare systems creates significant safety and security vulnerabilities that require urgent attention. Malicious actors may exploit these systems through adversarial attacks, such as data poisoning or prompt injections, to compromise patient confidentiality and treatment integrity. Additionally, inherent risks like hallucinations, algorithmic bias, and automation bias can lead to incorrect medical advice if not properly managed. To address these threats, the sources propose a multi-layered framework involving continuous monitoring, rigorous auditing, and clear regulatory oversight. Ultimately, establishing trustworthy AI in medicine depends on a collaborative effort between developers, healthcare providers, and policymakers to ensure patient welfare remains the primary priority.
References:
Clusmann J, Freyer O, Ostermann M, et al. Safety and security of large language models in healthcare[J]. Nature, 2026, 656(8128): 577-589.
Podden och tillhörande omslagsbild på den här sidan tillhör
淼淼Elva. Innehållet i podden är skapat av 淼淼Elva och inte av,
eller tillsammans med, Poddtoppen.
