Society Of General Internal Medicine Releases Position Statement On GenAI
The Society of General Internal Medicine has released a position statement on using generative AI (genAI) in healthcare, emphasizing the importance of collaboration between clinicians and technologists to enhance genAI’s role in clinical decision-making. The Society advises healthcare organizations to adopt genAI cautiously, avoiding rapid replacement of human clinicians, and urges clinicians to approach genAI like any other biomedical innovation by rigorously evaluating its evidence and clinical value. Given the risk of “errors and omissions,” the Society stresses that developers should not assume clinicians will simply “supervise” genAI but should instead meet the same high standards expected of clinicians by creating well-tested tools that enhance care rather than merely improving efficiency or market presence.
The Society advocates for strong collaboration between technologists, clinicians, and patients to ensure genAI outputs are accurate and trustworthy, providing clear mechanisms for verification. Healthcare organizations should seek both incremental and transformative uses of genAI while partnering with physicians to identify where it fits best within workflows, focusing particularly on areas like preventive care and chronic disease management where it may have the most significant impact. The Society reiterates that medicine remains fundamentally human and should be enhanced, not replaced, by technology.
As genAI expands in healthcare, regulatory bodies are also setting guidelines. In October, the FDA issued its stance on AI in healthcare, calling for coordinated oversight across industries and stressing the need for transparency and adaptable regulatory mechanisms. The FDA highlights balancing patient health outcomes with the financial interests of health systems and developers. Additionally, the Bipartisan Senate AI Working Group introduced an AI policy roadmap, advocating for research funding in transformative AI applications for medicine and transparency requirements for AI’s use in healthcare settings, including data transparency in AI model training.
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