• Bengesi, S. et al. Advancements in generative AI: a comprehensive review of GANs, GPT, autoencoders, diffusion model, and transformers. IEEE Access 12, 69812–69837 (2024).

    Article 

    Google Scholar
     

  • Sumner, J., Wang, Y., Tan, S. Y., Chew, E. H. H. & Wenjun Yip, A. Perspectives and experiences with large language models in health care: survey study. J. Med. Internet Res. 27, e67383 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Singhal, K. et al. Toward expert-level medical question answering with large language models. Nat. Med. 31, 943–950 (2025).

  • Amatriain, X. Transformer models: an introduction and catalog. Preprint at https://doi.org/10.48550/arXiv.2302.07730 (2023).

  • Tu, T. et al. Towards generalist biomedical AI. NEJM AI 1, AIoa2300138 (2024).

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

  • Ren, F. et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem. Sci. 14, 1443–1452 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • DeepSeek-AI et al. DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. Preprint at https://doi.org/10.48550/arXiv.2501.12948 (2025).

  • Zou, J. & Topol, E. J. The rise of agentic AI teammates in medicine. Lancet 405, 457 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Thirunavukarasu, A. J. Large language models will not replace healthcare professionals: curbing popular fears and hype. J. R. Soc. Med. 116, 181–182 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, P. The AI Revolution in Medicine: GPT-4 and Beyond (Pearson, 2023).

  • Lu, C. et al. The AI scientist: towards fully automated open-ended scientific discovery. Preprint at https://doi.org/10.48550/arXiv.2408.06292 (2024).

  • Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hornik, K., Stinchcombe, M. & White, H. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989).

    Article 

    Google Scholar
     

  • Wang, M. H. et al. Balancing accuracy and user satisfaction: the role of prompt engineering in AI-driven healthcare solutions. Front. Artif. Intell. 8, 1517918 (2025).

  • Hayati Rezvan, P., Lee, K. J. & Simpson, J. A. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med. Res. Methodol. 15, 30 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jarrett, D., Cebere, B. C., Liu, T., Curth, A. & Schaar, M. van der. HyperImpute: generalized iterative imputation with automatic model selection. In Proc. 39th International Conference on Machine Learning 9916–9937 (PMLR, 2022).

  • Giuffrè, M. & Shung, D. L. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. npj Digit.Med. 6, 186 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://doi.org/10.48550/arXiv.1312.6114 (2013).

  • Bredell, G., Flouris, K., Chaitanya, K., Erdil, E. & Konukoglu, E. Explicitly minimizing the blur error of variational autoencoders. Preprint at https://doi.org/10.48550/arXiv.2304.05939 (2023).

  • Goodfellow, I. J. et al. Generative adversarial networks. Preprint at https://doi.org/10.48550/arXiv.1406.2661 (2014).

  • Arora, A. & Arora, A. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc. J. 9, 190–193 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Webber, G. & Reader, A. J. Diffusion models for medical image reconstruction. BJRArtificial Intell. 1, ubae013 (2024).

    Article 

    Google Scholar
     

  • Vivekananthan, S. Comparative analysis of generative models: enhancing image synthesis with VAEs, GANs, and stable diffusion. Preprint at https://doi.org/10.48550/arXiv.2408.08751 (2024).

  • Khader, F. et al. Denoising diffusion probabilistic models for 3D medical image generation. Sci. Rep. 13, 7303 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Adams, L. C. et al. What does DALL-E 2 know about radiology? J. Med. Internet Res. 25, e43110 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pan, S. et al. Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model. Med. Phys. 51, 2538–2548 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Inkster, B., Sarda, S. & Subramanian, V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR MHealth UHealth 6, e12106 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meinert, E. et al. Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery. eClinicalMedicine 73, 102692 (2024).

  • Sackett, C., Harper, D. & Pavez, A. Do we dare use generative AI for mental health?. IEEE Spectr. 61, 42–47 (2024).

    Article 

    Google Scholar
     

  • Qiu, X. et al. Pre-trained models for natural language processing: A survey. Sci. China E Technol. Sci. 63, 1872–1897 (2020).

    Article 

    Google Scholar
     

  • Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training. Preprint at https://openai.com/research/language-unsupervised (2018).

  • Ouyang L, Wu J, Jiang X, et al. Training language models to follow instructions with human feedback. Preprint at https://doi.org/10.48550/arXiv.2203.02155 (2022).

  • Bai Y, Kadavath S, Kundu S, et al. Constitutional AI: harmlessness from AI feedback. Preprint at https://doi.org/10.48550/arXiv.2212.08073 (2022).

  • Shao Z, Wang P, Zhu Q, et al. DeepSeekMath: pushing the limits of mathematical reasoning in open language models. Preprint at https://doi.org/10.48550/arXiv.2402.03300 (2024).

  • Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023).

  • He, K. et al. Masked autoencoders are scalable vision learners. In Proc. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 15979–15988 (IEEE, 2022).

  • Bluethgen, C. et al. A vision–language foundation model for the generation of realistic chest X-ray images. Nat. Biomed. Eng. 9, 494–506 (2024).

  • Wang, X. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970–978 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, H. et al. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 181–188 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pai, S. et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6, 354–367 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jiao, J. et al. USFM: a universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Med. Image Anal. 96, 103202 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Qiu, J. et al. Development and validation of a multimodal multitask vision foundation model for generalist ophthalmic artificial intelligence. NEJM AI 1, AIoa2300221 (2024).

    Article 

    Google Scholar
     

  • Zhou, H.-Y. et al. A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nat. Biomed. Eng. 7, 743–755 (2023).

  • OpenAI. gpt-oss-120b & gpt-oss-20b Model Card. Preprint at https://cdn.openai.com/pdf/419b6906-9da6-406c-a19d-1bb078ac7637/oai_gpt-oss_model_card.pdf (2025).

  • Llama Team A@ M. Llama 4: leading intelligence. Unrivaled speed and efficiency. Meta https://llama.meta.com/ (2024).

  • Wei, J. et al. Chain-of-thought prompting elicits reasoning in large language models. In Proc. 36th International Conference on Neural Information Processing Systems 24824–24837 (Curran Associates, 2022).

  • Hosseini, S. & Seilani, H. The role of agentic AI in shaping a smart future: a systematic review. Array 26, 100399 (2025).

    Article 

    Google Scholar
     

  • Mukherjee, A. & Chang, H. H. Agentic AI: expanding the algorithmic frontier of creative problem solving. Preprint at https://doi.org/10.48550/arXiv.2502.00289 (2025).

  • Thirunavukarasu, A. J. et al. Clinical performance of automated machine learning: a systematic review. Ann. Acad. Med. Singap. 53, 187–207 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Moritz, M., Topol, E. & Rajpurkar, P. Coordinated AI agents for advancing healthcare. Nat. Biomed. Eng. 9, 432–438 (2025).

  • Tu, T. et al. Towards conversational diagnostic artificial intelligence. Nature 642, 442–450 (2025).

  • Ananta, I., Khetarpaul, S. & Sharma, D. Symptoms-disease detecting conversation agent using knowledge graphs. In Proc. 2024 Australasian Computer Science Week 98–107 (ACM, 2024).

  • Alghamdi, H. M. & Mostafa, A. Towards reliable healthcare LLM agents: a case study for pilgrims during Hajj. Information 15, 371 (2024).

    Article 

    Google Scholar
     

  • Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Magnini, M., Aguzzi, G. & Montagna, S. Open-source small language models for personal medical assistant chatbots. Intell.Based Med. 11, 100197 (2025).

    Article 

    Google Scholar
     

  • Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at https://doi.org/10.48550/arXiv.1503.02531 (2015).

  • Muennighoff, N. et al. s1: simple test-time scaling. Preprint at https://doi.org/10.48550/arXiv.2501.19393 (2025).

  • Kraljevic, Z. et al. Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study. Lancet Digit. Health 6, e281–e290 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, S. et al. A multimodal biomedical foundation model trained from fifteen million image–text pairs. NEJM AI 2, AIoa2400640 (2025).

    Article 

    Google Scholar
     

  • Tan, T. F. et al. Artificial intelligence and digital health in global eye health: opportunities and challenges. Lancet Glob. Health 11, e1432–e1443 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Soltan, A. A. S. et al. A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals. Lancet Digit. Health 6, e93–e104 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wahl, B., Cossy-Gantner, A., Germann, S. & Schwalbe, N. R. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob. Health 3, e000798 (2018).

  • Wang, X. et al. Beyond the limits: a survey of techniques to extend the context length in large language models. Preprint at https://doi.org/10.48550/arXiv.2402.02244 (2024).

  • Kaplan, J. et al. Scaling laws for neural language models. Preprint at https://doi.org/10.48550/arXiv.2001.08361 (2020).

  • Yang, R. et al. Retrieval-augmented generation for generative artificial intelligence in health care. npj Health Syst. 2, 1–5 (2025).

    Article 

    Google Scholar
     

  • Ng, F. Y. C. et al. Artificial intelligence education: an evidence-based medicine approach for consumers, translators, and developers. Cell Rep. Med. 4, 101230 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schubert, T., Oosterlinck, T., Stevens, R. D., Maxwell, P. H. & Schaar, M. van der. AI education for clinicians. eClinicalMedicine 79, 102968 (2025).

  • Shahsavar, Y. & Choudhury, A. User intentions to use ChatGPT for self-diagnosis and health-related purposes: cross-sectional survey study. JMIR Hum. Factors 10, e47564 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Blease, C. R., Locher, C., Gaab, J., Hägglund, M. & Mandl, K. D. Generative artificial intelligence in primary care: an online survey of UK general practitioners. BMJ Health Care Inform. 31, e101102 (2024).

  • Gillespie, N., Lockey, S., Ward, T., Macdade, A. & Hassed, G. Trust, attitudes and use of artificial intelligence: a global study 2025. The University of Melbourne and KPMG https://doi.org/10.26188/28822919 (2025).

  • Jayakumar, S. et al. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. npj Digit. Med. 5, 11 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gilson, A. et al. How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Med. Educ. 9, e45312 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kung, T. H. et al. Performance of ChatGPT on USMLE:pPotential for AI-assisted medical education using large language models. PLoS Digit. Health 2, e0000198 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).

  • Thirunavukarasu, A. J. et al. Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study. PLoS Digit. Health 3, e0000341 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ayers, J. W. et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern. Med. 183, 589–596 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huo, B. et al. Large language models for chatbot health advice studies: a systematic review. JAMA Netw. Open 8, e2457879 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thirunavukarasu, A. J. How can the clinical aptitude of AI assistants be assayed? J. Med. Internet Res. 25, e51603 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ebnali Harari, R., Altaweel, A., Ahram, T., Keehner, M. & Shokoohi, H. A randomized controlled trial on evaluating clinician-supervised generative AI for decision support. Int. J. Med. Inf. 195, 105701 (2025).

    Article 

    Google Scholar
     

  • Xie, Y. et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit. Health 2, e240–e249 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Goh, E. et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw. Open 7, e2440969 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Agarwal, N., Moehring, A., Rajpurkar, P. & Salz, T. Combining human expertise with artificial intelligence: experimental evidence from radiology. Working Paper 31422 (NBER, 2023).

  • Harris, E. Large language models answer medical questions accurately, but can’t match clinicians’ knowledge. JAMA 330, 792–794 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • OpenAI. Reasoning best practices. https://platform.openai.com/docs/guides/reasoning-best-practices (accessed 16 February 2025).

  • Bedi, S. et al. Testing and evaluation of health care applications of large language models: a systematic review. JAMA 1333, 319–328 (2024).


    Google Scholar
     

  • Kraljevic, Z., Yeung, J. A., Bean, D., Teo, J. & Dobson, R. J. Large language models for medical forecasting – Foresight 2. Preprint at https://doi.org/10.48550/arXiv.2412.10848 (2024).

  • Kampman, O. P. et al. Conversational self-play for discovering and understanding psychotherapy approaches. Preprint at https://doi.org/10.48550/arXiv.2503.16521 (2025).

  • Thirunavukarasu, A. J. & O’Logbon, J. The potential and perils of generative artificial intelligence in psychiatry and psychology. Nat. Ment. Health 2, 745–746 (2024).

  • Siddals, S., Torous, J. & Coxon, A. ‘It happened to be the perfect thing’: experiences of generative AI chatbots for mental health. npj Ment. Health Res. 3, 48 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roose, K. Can A.I. be blamed for a teen’s suicide? The New York Times (23 October 2024).

  • Heaukulani, C., Phang, Y. S., Weng, J. H., Lee, J. & Morris, R. J. T. Deploying AI methods for mental health in Singapore: from mental wellness to serious mental health conditions. Preprint at https://doi.org/10.2139/ssrn.4935469 (2024).

  • Kampman, O. P. et al. A multi-agent dual dialogue system to support mental health care providers. Preprint at https://doi.org/10.48550/arXiv.2411.18429 (2024).

  • Brügge, E. et al. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial. BMC Med. Educ. 24, 1391 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hale, J., Alexander, S., Wright, S. T. & Gilliland, K. Generative AI in undergraduate medical education: a rapid review. J. Med. Educ. Curric. Dev. 11, 23821205241266697 (2024).

    Article 

    Google Scholar
     

  • Afzal, S. et al. in Artificial Intelligence in Medicine (eds Michalowski, M. & Moskovitch, R.) 133–145 (Springer International, 2020).

  • Li, Y. S., Lam, C. S. N. & See, C. Using a machine learning architecture to create an AI-powered chatbot for anatomy education. Med. Sci. Educ. 31, 1729–1730 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Masters, K. Medical teacher’s first ChatGPT’s referencing hallucinations: lessons for editors, reviewers, and teachers. Med. Teach. 45, 673–675 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Herd, P. & Moynihan, D. Health care administrative burdens: centering patient experiences. Health Serv. Res. 56, 751–754 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, D. T. Y. et al. A scoping review of health information technology in clinician burnout. Appl. Clin. Inform. 12, 597–620 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Coiera, E. & Liu, S. Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep. Med. 3, 100860 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tierney, A. A. et al. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. Catal. Non-Issue Content 5, CAT.23.0404 (2024).


    Google Scholar
     

  • Cao, D. Y., Silkey, J. R., Decker, M. C. & Wanat, K. A. Artificial intelligence-driven digital scribes in clinical documentation: pilot study assessing the impact on dermatologist workflow and patient encounters. JAAD Int 15, 149–151 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Van Veen, D. et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat. Med. https://doi.org/10.1038/s41591-024-02855-5 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Decker, H. et al. Large language model−based chatbot vs surgeon-generated informed consent documentation for common procedures. JAMA Netw. Open 6, e2336997 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gimeno, A., Krause, K., D’Souza, S. & Walsh, C. G. Completeness and readability of GPT-4-generated multilingual discharge instructions in the pediatric emergency department. JAMIA Open 7, ooae050 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dong, H. et al. Automated clinical coding: what, why, and where we are?. npj Digit. Med. 5, 1–8 (2022).

    Article 

    Google Scholar
     

  • Soroush, A. et al. Large language models are poor medical coders — benchmarking of medical code querying. NEJM AI 1, AIdbp2300040 (2024).

  • Su, X. et al. Multimodal medical code tokenizer. Preprint at https://doi.org/10.48550/arXiv.2502.04397 (2025).

  • Sanghera, R. et al. High-performance automated abstract screening with large language model ensembles. J. Am. Med. Inform. Assoc. https://doi.org/10.1093/jamia/ocaf050 (2025).

  • Wornow, M. et al. The shaky foundations of large language models and foundation models for electronic health records. npjDigit. Med. 6, 135 (2023).


    Google Scholar
     

  • Rapp, J. T., Bremer, B. J. & Romero, P. A. Self-driving laboratories to autonomously navigate the protein fitness landscape. Nat. Chem. Eng. 1, 97–107 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Swanson, K., Wu, W., Bulaong, N. L., Pak, J. E. & Zou, J. The virtual lab of AI agents designs new SARS-CoV-2 nanobodies. Nature https://doi.org/10.1038/s41586-025-09442-9 (2025).

  • Tayebi Arasteh, S. et al. Large language models streamline automated machine learning for clinical studies. Nat. Commun. 15, 1603 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gao, S. et al. Empowering biomedical discovery with AI agents. Cell 187, 6125–6151 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Suchak, T. et al. Explosion of formulaic research articles, including inappropriate study designs and false discoveries, based on the NHANES US national health database. PLoS Biol. 23, e3003152 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alemohammad, S. et al. Self-consuming generative models go MAD. Preprint at https://doi.org/10.48550/arXiv.2307.01850 (2023).

  • Arora, A. & Arora, A. Synthetic patient data in health care: a widening legal loophole. Lancet 399, 1601–1602 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Thornton, J. M., Laskowski, R. A. & Borkakoti, N. AlphaFold heralds a data-driven revolution in biology and medicine. Nat. Med. 27, 1666–1669 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brixi, G. et al. Genome modeling and design across all domains of life with Evo 2. Arc Institute https://arcinstitute.org/manuscripts/Evo2 (accessed 20 February 2025.).

  • Skarlinski, M. D. et al. Language agents achieve superhuman synthesis of scientific knowledge. Preprint at https://doi.org/10.48550/arXiv.2409.13740 (2024).

  • Huang, K. et al. Automated hypothesis validation with agentic sequential falsifications. Preprint at https://doi.org/10.48550/arXiv.2502.09858 (2025).

  • Narayanan, S. et al. Aviary: training language agents on challenging scientific tasks. Preprint at https://doi.org/10.48550/arXiv.2412.21154 (2024).

  • Gottweis, J. et al. Towards an AI co-scientist. Preprint at https://doi.org/10.48550/arXiv.2502.18864 (2025).

  • Rajpurkar, P. & Topol, E. J. A clinical certification pathway for generalist medical AI systems. Lancet 405, 20 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Bedi, S., Shah, N. H. & Koyejo, S. Rethinking evaluation of large language models in healthcare. Competitive Policy International https://www.pymnts.com/cpi-posts/rethinking-evaluation-of-large-language-models-in-healthcare/ (2025).

  • Yim, D., Khuntia, J., Parameswaran, V. & Meyers, A. Preliminary evidence of the use of generative AI in health care clinical services: systematic narrative review. JMIR Med. Inform. 12, e52073 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thirunavukarasu, A. J. et al. Democratizing artificial intelligence imaging analysis with automated machine learning: tutorial. J. Med. Internet Res. 25, e49949 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Resnik, P. & Lin, J. in The Handbook of Computational Linguistics and Natural Language Processing 271–295 (Wiley Online Library, 2010).

  • Papineni, K., Roukos, S., Ward, T. & Zhu, W.-J. BLEU: a method for automatic evaluation of machine translation. In Proc. 40th Annual Meeting of the Association for Computational Linguistics (eds. Isabelle, P. et al.) 311–318 (Association for Computational Linguistics, 2002).

  • Banerjee, S. & Lavie, A. METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In Proc. ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization (eds. Goldstein, J. et al.) 65–72 (Association for Computational Linguistics, 2005).

  • Hossain, E. et al. Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review. Comput. Biol. Med. 155, 106649 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Haldar, R. & Mukhopadhyay, D. Levenshtein distance technique in dictionary lookup methods: an improved approach. Preprint at https://doi.org/10.48550/arXiv.1101.1232 (2011).

  • Ganesan, K. ROUGE 2.0: updated and improved measures for evaluation of summarization tasks. Preprint at https://doi.org/10.48550/arXiv.1803.01937 (2018).

  • Rei, R., Stewart, C., Farinha, A. C. & Lavie, A. COMET: a neural framework for MT evaluation. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (eds. Webber, B. et al.) 2685–2702 (Association for Computational Linguistics, 2020).

  • Tan, T. F. et al. A proposed S.C.O.R.E. evaluation framework for large language models – safety, consensus & context, objectivity, reproducibility and explainability. Preprint at https://doi.org/10.48550/arXiv.2407.07666 (2024).

  • Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q. & Artzi, Y. BERTScore: evaluating text generation with BERT. Preprint at https://doi.org/10.48550/arXiv.1904.09675 (2019).

  • Liu, Y. et al. G-Eval: NLG evaluation using GPT-4 with better human alignment. Preprint at https://doi.org/10.48550/arXiv.2303.16634 (2023).

  • Fu J, Ng SK, Jiang Z, Liu P. GPTScore: evaluate as you desire. Preprint at https://doi.org/10.48550/arXiv.2302.04166 (2023).

  • Lees, A. et al. A new generation of perspective API: efficient multilingual character-level transformers. In Proc. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 3197–3207 (ACM, 2022).

  • Teo, C. T. H., Abdollahzadeh, M. & Cheung, N.-M. On measuring fairness in generative models. Preprint at https://doi.org/10.48550/arXiv.2310.19297 (2023).

  • Min, S. et al. FActScore: fine-grained atomic evaluation of factual precision in long form text generation. Preprint at https://doi.org/10.48550/arXiv.2305.14251 (2023).

  • Xu, W., Napoles, C., Pavlick, E., Chen, Q. & Callison-Burch, C. Optimizing statistical machine translation for text simplification. Trans. Assoc. Comput. Linguist. 4, 401–415 (2016).

    Article 

    Google Scholar
     

  • Gu, J. et al. A survey on LLM-as-a-judge. Preprint at https://doi.org/10.48550/arXiv.2411.15594 (2025).

  • Croxford, E. et al. Automating evaluation of AI text generation in healthcare with a large language model (LLM)-as-a-judge. Preprint at MedRxiv https://doi.org/10.1101/2025.04.22.25326219 (2025).

  • Abbasian, M. et al. Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative. AI. npj Digit. Med. 7, 82 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Bommasani, R., Liang, P. & Lee, T. Holistic evaluation of language models. Ann. N Y Acad. Sci. 1525, 140–146 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Han, R. et al. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit. Health 6, e367–e373 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gallifant, J. et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat. Med. 31, 60–69 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schulz, K. F., Altman, D. G., Moher, D. & & CONSORT Group CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ 340, c332 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huo, B. et al. Reporting guidelines for chatbot health advice studies: explanation and elaboration for the Chatbot Assessment Reporting Tool (CHART). BMJ 390, e083305 (2025).

    Article 

    Google Scholar
     

  • Chan, A.-W. et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann. Intern. Med. 158, 200–207 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bedi, S. et al. MedHELM: holistic evaluation of large language models for medical tasks. Preprint at https://doi.org/10.48550/arXiv.2505.23802 (2025).

  • Quin, F., Weyns, D., Galster, M. & Silva, C. C. A/B testing: a systematic literature review. J. Syst. Softw. 211, 112011 (2024).

    Article 

    Google Scholar
     

  • Austrian, J. et al. Applying A/B testing to clinical decision support: rapid randomized controlled trials. J. Med. Internet Res. 23, e16651 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Priestman, W. et al. What to expect from electronic patient record system implementation: lessons learned from published evidence. BMJ Health Care Inform. 25, 92–104 (2018).

  • Ning, Y. et al. Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist. Lancet Digit. Health 6, e848–e856 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ning, Y. et al. An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI. Nat. Med. 30, 3038–3039 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ganapathi, S. et al. Tackling bias in AI health datasets through the STANDING Together initiative. Nat. Med. 28, 2232–2233 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Khanna, N. N. et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare 10, 2493 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pagallo, U. et al. The underuse of AI in the health sector: opportunity costs, success stories, risks and recommendations. Health Technol. 14, 1–14 (2024).

    Article 

    Google Scholar
     

  • Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 368, m689 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huo, B. et al. Reporting standards for the use of large language model-linked chatbots for health advice. Nat. Med. 29, 2988 (2023).

  • Council of the European Union, European Parliament. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 Laying down Harmonised Rules on Artificial Intelligence and Amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA Relevance). PE/24/2024/REV/1 (2024).

  • LCM team et al. Large concept models: language modeling in a sentence representation space. Preprint at https://doi.org/10.48550/arXiv.2412.08821 (2024).

  • Shen, M., Li, Y., Chen, L. & Yang, Q. From mind to machine: the rise of manus AI as a fully autonomous digital agent. Preprint at https://doi.org/10.48550/arXiv.2505.02024 (2025).