The advent of artificial intelligence (AI) in generating clinical summaries has presented unparalleled opportunities and significant challenges. We must navigate these waters with caution and foresight as we stand on the cusp of integrating these technologies into everyday clinical practice.

A recent article in JAMA highlights the potential of large language models (LLMs) like ChatGPT-4 in streamlining information gathering from electronic health records (EHR). It underscores the necessity for transparent development of standards for LLM-generated clinical summaries and pragmatic clinical studies to ensure their safe and prudent deployment. This resonates deeply with my conviction that as we embrace AI in clinical settings, we must prioritize accuracy, transparency, and standards to mitigate misinformation risks.

AI-generated clinical summaries can significantly impact patient care and medical research. They promise to alleviate physician burnout and enhance clinical decision-making by providing succinct, relevant, and accurate summaries of complex patient data. However, without stringent standards and clear indications that these summaries are AI-generated, there is a risk of introducing biases and inaccuracies into patient records—a scenario we must diligently avoid to ensure the highest patient care and safety standards.

Miseducation of Google's AI

Google’s recent kerfuffle over the release of Gemini, its new AI application, only emphasizes the importance of data quality when training large-language models. Google got in trouble trying to correct the biases, prejudices, and stereotypes in the internet data used to train Gemini. Internet data represents both the good and bad aspects of human existence. For those creating AI tools, what is their responsibility to limit harm, and should they try at all?

Automation Bias: A Short Cut to Medical Errors

To navigate the world, we generalize situations to reduce the burden of thinking through every decision point. While AI offers an assistive tool to help physicians with decision-making, it becomes a threat to patient care when automation bias takes hold. Clinical workflows that include AI must present “human stops,” so the clinicians are forced to review the AI recommendations rather than automatically approve them.

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