Publication Date
8-1-2026
Journal
Lancet Digital Health
DOI
10.1016/j.landig.2026.101011
PMID
42259738
Abstract
In this Viewpoint, we advocate for direct tokenisation of medical data by breaking them into discrete units, such as laboratory results, medications, and vital signs, similar to word tokenisation in language models. This approach enables transformer-based models to learn from the temporal structure of patient health timelines without relying on textual translation, potentially leading to more accurate and personalised care. Enhanced Transformer for Health Outcome Simulation, an example of a model that uses tokenisation, forecasts health timelines and supports clinical decision making using tokenised medical records. We outline a privacy-preserving model-sharing framework, in which models are trained locally and only trained models-not sensitive data-are shared, allowing collaborative development across institutions. We also emphasise that access to large, diverse datasets enhances fairness, generalisability, and equity in health-care generative artificial intelligence. Although challenges such as data complexity and interpretability remain, this Viewpoint underscores that embracing tokenised representations opens a path towards scalable, multimodal, and equitable artificial intelligence in medicine.
Keywords
Generative Artificial Intelligence, Humans, Artificial Intelligence, Electronic Health Records, Large Language Models
Published Open-Access
yes
Recommended Citation
Arkadiusz Sitek and David W Bates, "Beyond Language: Generative Artificial Intelligence as a General Computing Model for Medicine" (2026). Faculty, Staff and Students Publications. 8522.
https://digitalcommons.library.tmc.edu/baylor_docs/8522