Large language models (LLMs) are increasingly applied in medical documentation and have been proposed for clinical decision support. We argue that the future for LLMs in medicine must be based on transparent and controllable open-source models. Openness enables medical tool developers to control the safety and quality of underlying AI models, while also allowing healthcare professionals to hold these models accountable. For these reasons, the future is open.

John J. Hopfield and Geoffrey E. Hinton were awarded the 2024 Nobel Prize in Physics for developing machine learning technology using artificial neural networks. In Chemistry it was awarded to Demis Hassabis and John M. Jumper for developing an AI algorithm that solved the 50-year protein structure prediction challenge. This highlights AI’s impact on science, medicine and society; however, the winners acknowledge ethical aspects of AI that must be considered.

The introduction of the electronic health record was heralded as a technology solution to improve care quality and efficiency, but these tools have contributed to increased administrative burden and burnout for clinicians. Today, artificial intelligence is receiving much of the same attention and promises as electronic health records. Can healthcare learn from the failures of electronic health records to maximize the potential of artificial intelligence?