Towards Generalist Biomedical AI
Open accessAbstract
BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, and interpret these data might better enable impactful applications ranging from scientific discovery to care delivery.MethodsTo catalyze development of these models, we curated MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks, such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduced Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. To further probe the capabilities and limitations of Med-PaLM M, we conducted a radiologist evaluation of model-generated (and human) chest x-ray reports.ResultsWe observed encouraging performance across model scales. Med-PaLM M reached performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. In a side-by-side ranking on 246 retrospective chest x-rays, clinicians expressed a pairwise preference for Med-PaLM Multimodal reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility.ConclusionsAlthough considerable work is needed to validate these models in real-world cases and understand if cross-modality generalization is possible, our results represent a milestone toward the development of generalist biomedical artificial intelligence systems. (Funded by Alphabet Inc. and/or a subsidiary thereof.)
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Generative vision language models such as Med-PaLM M, LLaVA-Med, or PathChat can output text, but only at the patch level for small image regions up to 1024 × 1024 pixels.
“Generative vision language models such as Med-PaLM M14, LLaVA-Med15, or PathChat16 can output text, but only at the patch level for small image regions up to 1024 × 1024 pixels.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [14]
Despite advances with models such as Med-PaLM M or Med-Gemini, the complexities to develop a generalist foundation LLM that truly performs on par with precision medicine tools remain a substantial challenge.
“Despite advances with models such as Med-PaLM M11 or Med-Gemini12, the complexities to develop a generalist foundation LLM that truly performs on par with precision medicine tools remain a substantial challenge.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [11]
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- Authors
- Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., Palepu, A., Mustafa, B., Chowdhery, A., Liu, Y., Kornblith, S., Fleet, D., Mansfield, P., Prakash, S., Wong, R., Virmani, S., Semturs, C., Mahdavi, S. S., Green, B., Dominowska, E., Arcas, B. A. Y., Barral, J., Webster, D., Corrado, G. S., Matias, Y., Singhal, K., Florence, P., Karthikesalingam, A., & Natarajan, V.
- Affiliations
- Google (United States) (US)
- Google DeepMind (United Kingdom) (GB)
- Google DeepMind
- Google Research
- ORCID
- Tu, Tao
- Azizi, Shekoofeh
- Driess, Danny
- Schaekermann, Mike
- Amin, Mohamed
- Chang, Pi-Chuan
- Carroll, Andrew
- Lau, Charles
- Tanno, Ryutaro
- Ktena, Ira
- Palepu, Anil
- Mustafa, Basil
- Chowdhery, Aakanksha
- Liu, Yun
- Kornblith, Simon
- Fleet, David
- Mansfield, Philip
- Prakash, Sushant
- Wong, Renee
- Virmani, Sunny
- Semturs, Christopher
- Mahdavi, S. Sara
- Green, Bradley
- Dominowska, Ewa
- Arcas, Blaise Aguera y
- Barral, Joelle
- Webster, Dale
- Corrado, Greg S.
- Matias, Yossi
- Singhal, Karan
- Florence, Pete
- Karthikesalingam, Alan
- Natarajan, Vivek
- Venue
- NEJM AI
- Publisher
- Massachusetts Medical Society
- Published
- 2024-02-22
- Volume
- 1
- Issue
- 3
- Pages
- AIoa2300138-AIoa2300138
- ISSN
- 2836-9386 (ISSN-L)
- OpenAlex
- W4392044798
- Cited by
- 483
- References
- 27
- Citations by year
- 2026: 152 · 2025: 193 · 2024: 125 · 2023: 12
- FWCI
- 44.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- cc-by
- Locations
- NEJM AI (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- DeepMind
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- medical question answering, radiology report generation, chest X-ray reports, mammography interpretation, variant calling, cross-modality generalization
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