Abstract Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task‐specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Benedict Emoekabu, Aswanth Krishnan, Tanya Gupta, Macjonathan Okereke, Amir Mohammad Elahi, Mehrdad Asgari, J. Eberhardt, Maximilian Greiner, Caroline T. Holick, Christina Glaubitz, Tim Hoffmann, Lea C. Klepsch, Yannik Köster, Fabian Alexander Kreth, Jakob Meyer, Santiago Miret, Michael Ringleb, Nicole C. Roesner, Ulrich Sigmar Schubert, Leanne M. Stafast, Dinga Wonanke, Michael Pieler,
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