Generative artificial intelligence in medicine
Abstract
Generative artificial intelligence (GAI) can automate a growing number of biomedical tasks, ranging from clinical decision support to design and analysis of research studies. GAI uses machine learning and transformer model architectures to generate useful text, images and sound data in response to user queries. While previous biomedical deep-learning applications have used general-purpose datasets and enormous volumes of labeled data for training, evidence now suggests that GAI models may perform better while requiring less training data-for example, using smaller, domain-specific datasets. Moreover, AI techniques have progressed from fully supervised training to less label-intensive approaches, such as weakly supervised or unsupervised fine-tuning and reinforcement learning. Recent iterations of GAI, such as agents, mixture-of-expert models and reasoning models, have further extended their capabilities to assist with complex and multistage tasks. Here, we provide an overview of recent technical advancements in GAI. We explore the potential of the latest generation of models to improve healthcare for clinicians and patients, and discuss validation approaches using specific examples to illustrate challenges and opportunities for further work.
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Large language models, computer vision systems, and clinical decision-support systems have demonstrated substantial clinical value across medical specialties.
“Large language models (LLMs), computer vision systems, and clinical decision-support systems (CDSS) have demonstrated substantial clinical value across medical specialties 1 -4 .”
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · cited 1× · see citing works
“Alpha-Fold and its daughter models accurately predict protein structures and can now model protein–protein interactions on-demand; these investigations previously required extensive laboratory experimentation 7,8,121 .”
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
the CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-18 · Cited by 13,371 · see citing works
Masked Autoencoders Are Scalable Vision Learners
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01553 · Cited by 8,489 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613 · cited 3× · see citing works
“…ability, one study directly compared a GAI model and clinicians in responding to patient enquiries posted on a social-media forum—and found that the model provided higher-quality and more-empathetic responses than clinicians did (assessed in a blinded fashion by healthcare professionals) 79 .”
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · cited 1× · see citing works
“The ongoing development of models with growing reasoning and response abilities has generated optimism that ‘generalist’ medical AI— essentially medical foundation models that can automate diverse medical tasks with little or no specific training—will begin to be deployed in clinical contexts 57 .”
Scaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · Cited by 1,749 · cited 1× · see citing works
“Smaller models also tend to produce less-desirable outputs in terms of responding appropriately and flexibly to queries, as well as raw recall of accurate specialist knowledge 67 .”
Toward expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734 · see citing works
Self-citationInkster, B., Sarda, S., & Subramanian, V. (2018) · doi:10.2196/preprints.12106 · Cited by 1,063 · see citing works
A foundation model for generalizable disease detection from retinal images
Zhou, Y., Chia, M. A., Wagner, S. K., Ayhan, M. S., Williamson, D. J., Struyven, R. R., Liu, T., Xu, M., Lozano, M. G., Woodward-Court, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06555-x · Cited by 1,014 · see citing works
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., & Zhu, C. (2023) · arXiv · doi:10.48550/arxiv.2303.16634 · Cited by 948 · see citing works
A whole-slide foundation model for digital pathology from real-world data
Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921 · see citing works
Adapted large language models can outperform medical experts in clinical text summarization
Van Veen, D., Van Uden, C., Blankemeier, L., Delbrouck, J.-B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerová, A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02855-5 · Cited by 891 · cited 1× · see citing works
“GAI exhibits remarkable summarization ability, with one study demonstrating superiority to clinicians in terms of quality and efficiency 106 .”
Self-citationLarge Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial
Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.40969 · Cited by 724 · see citing works
Sequence modeling and design from molecular to genome scale with Evo
Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., et al. (2024) · Science · doi:10.1126/science.ado9336 · Cited by 580 · see citing works
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.12948 · Cited by 538 · see citing works
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.14334 · Cited by 483 · see citing works
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI · doi:10.1056/aioa2300138 · Cited by 483 · see citing works
Holistic Evaluation of Language Models
Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480 · cited 1× · see citing works
“A multi-metric approach to evaluation is very likely necessary to overcome the limitations of any single system 151 .”
Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106649 · Cited by 407 · see citing works
Towards conversational diagnostic artificial intelligence
Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08866-7 · Cited by 366 · see citing works
Han, R., Acosta, J. N., Shakeri, Z., Ioannidis, J. P. A., Topol, E. J., & Rajpurkar, P. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00047-5 · Cited by 336 · cited 2× · see citing works
“First, although much attention has been placed on model development, subsequent deployment in real-world settings is relatively understudied 80,152,166 .”
InfluentialBai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., & Kaplan, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.08073 · Cited by 326 · cited 2× · see citing works
“Conversely, state-of-the-art mixture-of-expert models (for example, DeepSeek-R1) dispense with the critic model required for RLHF or RLAIF, in favor of a group relative policy optimization (GPRO) process—in which multiple outputs are directly compared with one another to encourage production of outputs with favored ch…”
Empowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315 · see citing works
A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs
Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., et al. (2025) · NEJM AI · doi:10.1056/aioa2400640 · Cited by 283 · cited 1× · see citing works
“Such fine-tuning is typically limited by lack of access to patient data owing to data-privacy governance, although ongoing efforts aim to broaden access to large, multimodal clinical datasets 62 .”
Large Language Models for Chatbot Health Advice Studies: A Systematic Review
Huo, B., Boyle, A., Marfo, N., Tangamornsuksan, W., Steen, J. P., McKechnie, T., Lee, Y., Mayol, J., Antoniou, S. A., Thirunavukarasu, A. J., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.57879 · Cited by 283 · see citing works
Self-citationThe role of agentic AI in shaping a smart future: A systematic review
Hosseini, S., & Seilani, H. (2025) · Array · doi:10.1016/j.array.2025.100399 · Cited by 280 · see citing works
Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI
Abbasian, M., Khatibi, E., Azimi, I., Oniani, D., Shakeri Hossein Abad, Z., Thieme, A., Sriram, R., Yang, Z., Wang, Y., Lin, B., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01074-z · Cited by 213 · cited 1× · see citing works
“For a comprehensive overview of foundational metrics for clinical GAI assessment, Abbasian et al. have provided a summary grouped under the headings of accuracy, trustworthiness, empathy and performance 150 .”
Foundation Model for Cancer Imaging Biomarkers
Pai, S., Bontempi, D., Hadzic, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00807-9 · Cited by 199 · see citing works
AlphaFold heralds a data-driven revolution in biology and medicine
Thornton, J. M., Laskowski, R. A., & Borkakoti, N. (2021) · Nature Medicine · doi:10.1038/s41591-021-01533-0 · Cited by 185 · see citing works
The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies
Swanson, K., Wu, W., Bulaong, N. L., Pak, J. E., & Zou, J. (2025) · Nature · doi:10.1038/s41586-025-09442-9 · Cited by 169 · cited 1× · see citing works
“…demonstrate GAI’s potential in research: appraising and designing new machine-learning architectures, linking with a robotic system to fully automate theorizing and proving structure–function relationships of proteins, and even designing therapeutics that could treat disease 14,114,115 .”
Arora, A., & Arora, A. (2022) · Future Healthcare Journal · doi:10.7861/fhj.2022-0013 · Cited by 161 · see citing works
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.2139/ssrn.4505053 · Cited by 161 · see citing works
Brügge, E., Ricchizzi, S., Arenbeck, M., Keller, M. N., Schur, L., Stummer, W., Holling, M., Lu, M. H., & Darici, D. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06399-7 · Cited by 137 · cited 1× · see citing works
“Differences emerged after just four sessions—highlighting the potential of foundation models to improve the provision of tailored clinical education 96 .”
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. (2024) · arXiv · doi:10.48550/arxiv.2408.06292 · Cited by 118 · cited 2× · see citing works
“Possible functions range from simple tasks, such as reformatting text, to assistance with technical tasks, such as coding and even modeling to simulate experiments and thereby maximize the efficiency of bench work 14 .”
Kraljevic, Z., Bean, D., Shek, A., Bendayan, R., Hemingway, H., Yeung, J. A., Deng, A., Balston, A., Ross, J., Idowu, E., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00025-6 · Cited by 118 · cited 2× · see citing works
“Domain-specific fine-tuning can facilitate superior performance in clinical tasks relative to state-of-the-art models 61 .”
Large language models streamline automated machine learning for clinical studies
Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S. (2024) · Nature Communications · doi:10.1038/s41467-024-45879-8 · Cited by 114 · see citing works
Ng, F. Y. C., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101230 · Cited by 113 · see citing works
Self-citationA vision–language foundation model for the generation of realistic chest X-ray images
Bluethgen, C., Chambon, P., Delbrouck, J.-B., van der Sluijs, R., Połacin, M., Zambrano Chaves, J. M., Abraham, T. M., Purohit, S., Langlotz, C. P., & Chaudhari, A. S. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01246-y · Cited by 108 · see citing works
Self-citationSchubert, T., Oosterlinck, T., Stevens, R. D., Maxwell, P. H., & van der Schaar, M. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2024.102968 · Cited by 103 · see citing works
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y. K., Wu, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2402.03300 · Cited by 101 · see citing works
Suchak, T., Aliu, A. E., Harrison, C., Zwiggelaar, R., Geifman, N., & Spick, M. (2025) · PLOS Biology · doi:10.1371/journal.pbio.3003152 · Cited by 95 · see citing works
Reporting standards for the use of large language model-linked chatbots for health advice.
Huo, B., Cacciamani, G. E., Collins, G. S., McKechnie, T., Lee, Y., & Guyatt, G. (2023) · Nature Medicine · doi:10.1038/s41591-023-02656-2 · Cited by 53 · see citing works
Language agents achieve superhuman synthesis of scientific knowledge
Skarlinski, M. D., Cox, S., Laurent, J. M., Braza, J. D., Hinks, M., Hammerling, M. J., Ponnapati, M., Rodriques, S. G., & White, A. D. (2024) · arXiv · doi:10.48550/arxiv.2409.13740 · Cited by 49 · cited 1× · see citing works
“Various research models offer synthesis functionality to provide a preliminary overview of any field of inquiry, and comparative results suggest that these overviews are of comparable quality to summaries produced by humans, such as on Wikipedia articles 125 .”
Self-consuming generative models go MAD
Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A. I., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. G. (2023) · arXiv · doi:10.48550/arxiv.2307.01850 · Cited by 44 · see citing works
Croxford, E., Gao, Y., First, E., Pellegrino, N., Schnier, M., Caskey, J., Oguss, M., Wills, G., Chen, G., Dligach, D., et al. (2025) · doi:10.1101/2025.04.22.25326219 · Cited by 25 · see citing works
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv · doi:10.48550/arxiv.2305.14251 · Cited by 14 · see citing works
Do We Dare Use Generative AI for Mental Health?
Sackett, C., Harper, D., & Pavez, A. (2024) · IEEE Spectrum · doi:10.1109/mspec.2024.10551790 · Cited by 13 · see citing works
Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., & Hashimoto, T. (2025) · arXiv · doi:10.48550/arxiv.2501.19393 · Cited by 12 · cited 1× · see citing works
“Smaller models with comparable performance to that of industrial flagship foundation models can be engineered relatively simply through a process called model distillation, whereby a small, open-source language model is fine-tuned on a set of outputs generated by flagship models 59,60 .”
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AI-induced never-skilling in medical education
Ke, Y., Jin, L., Ong, J. C. L., Thirunavukarasu, A. J., Car, J., Cheung, C. Y., Tham, Y. C., Ting, D. S. W., Ong, M. E. H., Compton, S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04438-y · Cited by 35 · see citing works
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- Authors
- Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., Shah, N. H., Langlotz, C. P., & Ting, D. S. W.
- Affiliations
- SingHealth (SG)
- University of Oxford (GB)
- Singapore National Eye Center (SG)
- Stanford Medicine (US)
- University of Calgary (CA)
- University of Nottingham (GB)
- Ministry of Health (SG)
- Corresponding author
- Ting, Darren Shu Jeng
- Ting, Daniel Shu Wei
- ORCID
- Venue
- Nature Medicine
- Published
- 2025-10
- Volume
- 31
- Issue
- 10
- Pages
- 3270-3282
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4414849058
- Cited by
- 119
- References
- 54
- Citations by year
- 2026: 106 · 2025: 12 · 2022: 1
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- MOH | National Medical Research Council (MOH-000655-00; MOH-001014-00)
- Agency for Science, Technology and Research (H20C6a0032)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- generative AI, clinical decision support, transformer models, domain-specific datasets, unsupervised fine-tuning, reinforcement learning, AI agents, reasoning models, medical applications, AI validation
- MeSH terms
- Machine Learning, Deep Learning, Generative Artificial Intelligence, Artificial Intelligence, Humans, Medicine
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