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).
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).
Google Scholar
Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).
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 (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).
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).
Google Scholar
DeepSeek-AI et al. DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. Preprint at (2025).
Zou, J. & Topol, E. J. The rise of agentic AI teammates in medicine. Lancet 405, 457 (2025).
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).
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 (2024).
Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).
Google Scholar
Hornik, K., Stinchcombe, M. & White, H. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989).
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).
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).
Google Scholar
Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at (2013).
Bredell, G., Flouris, K., Chaitanya, K., Erdil, E. & Konukoglu, E. Explicitly minimizing the blur error of variational autoencoders. Preprint at (2023).
Goodfellow, I. J. et al. Generative adversarial networks. Preprint at (2014).
Arora, A. & Arora, A. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc. J. 9, 190–193 (2022).
Google Scholar
Webber, G. & Reader, A. J. Diffusion models for medical image reconstruction. BJRArtificial Intell. 1, ubae013 (2024).
Google Scholar
Vivekananthan, S. Comparative analysis of generative models: enhancing image synthesis with VAEs, GANs, and stable diffusion. Preprint at (2024).
Khader, F. et al. Denoising diffusion probabilistic models for 3D medical image generation. Sci. Rep. 13, 7303 (2023).
Google Scholar
Adams, L. C. et al. What does DALL-E 2 know about radiology? J. Med. Internet Res. 25, e43110 (2023).
Google Scholar
Pan, S. et al. Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model. Med. Phys. 51, 2538–2548 (2024).
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).
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).
Google Scholar
Qiu, X. et al. Pre-trained models for natural language processing: A survey. Sci. China E Technol. Sci. 63, 1872–1897 (2020).
Google Scholar
Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training. Preprint at (2018).
Ouyang L, Wu J, Jiang X, et al. Training language models to follow instructions with human feedback. Preprint at (2022).
Bai Y, Kadavath S, Kundu S, et al. Constitutional AI: harmlessness from AI feedback. Preprint at (2022).
Shao Z, Wang P, Zhu Q, et al. DeepSeekMath: pushing the limits of mathematical reasoning in open language models. Preprint at (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).
Google Scholar
Xu, H. et al. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 181–188 (2024).
Google Scholar
Pai, S. et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6, 354–367 (2024).
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).
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).
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 (2025).
Llama Team A@ M. Llama 4: leading intelligence. Unrivaled speed and efficiency. Meta (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).
Google Scholar
Mukherjee, A. & Chang, H. H. Agentic AI: expanding the algorithmic frontier of creative problem solving. Preprint at (2025).
Thirunavukarasu, A. J. et al. Clinical performance of automated machine learning: a systematic review. Ann. Acad. Med. Singap. 53, 187–207 (2024).
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).
Google Scholar
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).
Google Scholar
Magnini, M., Aguzzi, G. & Montagna, S. Open-source small language models for personal medical assistant chatbots. Intell.Based Med. 11, 100197 (2025).
Google Scholar
Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at (2015).
Muennighoff, N. et al. s1: simple test-time scaling. Preprint at (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).
Google Scholar
Zhang, S. et al. A multimodal biomedical foundation model trained from fifteen million image–text pairs. NEJM AI 2, AIoa2400640 (2025).
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).
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).
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 (2024).
Kaplan, J. et al. Scaling laws for neural language models. Preprint at (2020).
Yang, R. et al. Retrieval-augmented generation for generative artificial intelligence in health care. npj Health Syst. 2, 1–5 (2025).
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).
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).
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 (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).
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).
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).
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).
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).
Google Scholar
Huo, B. et al. Large language models for chatbot health advice studies: a systematic review. JAMA Netw. Open 8, e2457879 (2025).
Google Scholar
Thirunavukarasu, A. J. How can the clinical aptitude of AI assistants be assayed? J. Med. Internet Res. 25, e51603 (2023).
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).
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).
Google Scholar
Goh, E. et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw. Open 7, e2440969 (2024).
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).
Google Scholar
OpenAI. 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).
Kraljevic, Z., Yeung, J. A., Bean, D., Teo, J. & Dobson, R. J. Large language models for medical forecasting – Foresight 2. Preprint at (2024).
Kampman, O. P. et al. Conversational self-play for discovering and understanding psychotherapy approaches. Preprint at (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).
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 (2024).
Kampman, O. P. et al. A multi-agent dual dialogue system to support mental health care providers. Preprint at (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).
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).
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).
Google Scholar
Masters, K. Medical teacher’s first ChatGPT’s referencing hallucinations: lessons for editors, reviewers, and teachers. Med. Teach. 45, 673–675 (2023).
Google Scholar
Herd, P. & Moynihan, D. Health care administrative burdens: centering patient experiences. Health Serv. Res. 56, 751–754 (2021).
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).
Google Scholar
Coiera, E. & Liu, S. Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep. Med. 3, 100860 (2022).
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).
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).
Google Scholar
Van Veen, D. et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat. Med. (2024).
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).
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).
Google Scholar
Dong, H. et al. Automated clinical coding: what, why, and where we are?. npj Digit. Med. 5, 1–8 (2022).
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 (2025).
Sanghera, R. et al. High-performance automated abstract screening with large language model ensembles. J. Am. Med. Inform. Assoc. (2025).
Wornow, M. et al. The shaky foundations of large language models and foundation models for electronic health records. npjDigit. Med. 6, 135 (2023).
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).
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 (2025).
Tayebi Arasteh, S. et al. Large language models streamline automated machine learning for clinical studies. Nat. Commun. 15, 1603 (2024).
Google Scholar
Gao, S. et al. Empowering biomedical discovery with AI agents. Cell 187, 6125–6151 (2024).
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).
Google Scholar
Alemohammad, S. et al. Self-consuming generative models go MAD. Preprint at (2023).
Arora, A. & Arora, A. Synthetic patient data in health care: a widening legal loophole. Lancet 399, 1601–1602 (2022).
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).
Google Scholar
Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025).
Google Scholar
Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).
Google Scholar
Brixi, G. et al. Genome modeling and design across all domains of life with Evo 2. Arc Institute (accessed 20 February 2025.).
Skarlinski, M. D. et al. Language agents achieve superhuman synthesis of scientific knowledge. Preprint at (2024).
Huang, K. et al. Automated hypothesis validation with agentic sequential falsifications. Preprint at (2025).
Narayanan, S. et al. Aviary: training language agents on challenging scientific tasks. Preprint at (2024).
Gottweis, J. et al. Towards an AI co-scientist. Preprint at (2025).
Rajpurkar, P. & Topol, E. J. A clinical certification pathway for generalist medical AI systems. Lancet 405, 20 (2025).
Google Scholar
Bedi, S., Shah, N. H. & Koyejo, S. Rethinking evaluation of large language models in healthcare. Competitive Policy International (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).
Google Scholar
Thirunavukarasu, A. J. et al. Democratizing artificial intelligence imaging analysis with automated machine learning: tutorial. J. Med. Internet Res. 25, e49949 (2023).
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).
Google Scholar
Haldar, R. & Mukhopadhyay, D. Levenshtein distance technique in dictionary lookup methods: an improved approach. Preprint at (2011).
Ganesan, K. ROUGE 2.0: updated and improved measures for evaluation of summarization tasks. Preprint at (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 (2024).
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q. & Artzi, Y. BERTScore: evaluating text generation with BERT. Preprint at (2019).
Liu, Y. et al. G-Eval: NLG evaluation using GPT-4 with better human alignment. Preprint at (2023).
Fu J, Ng SK, Jiang Z, Liu P. GPTScore: evaluate as you desire. Preprint at (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 (2023).
Min, S. et al. FActScore: fine-grained atomic evaluation of factual precision in long form text generation. Preprint at (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).
Google Scholar
Gu, J. et al. A survey on LLM-as-a-judge. Preprint at (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 (2025).
Abbasian, M. et al. Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative. AI. npj Digit. Med. 7, 82 (2024).
Google Scholar
Bommasani, R., Liang, P. & Lee, T. Holistic evaluation of language models. Ann. N Y Acad. Sci. 1525, 140–146 (2023).
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).
Google Scholar
Gallifant, J. et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat. Med. 31, 60–69 (2025).
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).
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).
Google Scholar
Chan, A.-W. et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann. Intern. Med. 158, 200–207 (2013).
Google Scholar
Bedi, S. et al. MedHELM: holistic evaluation of large language models for medical tasks. Preprint at (2025).
Quin, F., Weyns, D., Galster, M. & Silva, C. C. A/B testing: a systematic literature review. J. Syst. Softw. 211, 112011 (2024).
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).
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).
Google Scholar
Ning, Y. et al. An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI. Nat. Med. 30, 3038–3039 (2024).
Google Scholar
Ganapathi, S. et al. Tackling bias in AI health datasets through the STANDING Together initiative. Nat. Med. 28, 2232–2233 (2022).
Google Scholar
Khanna, N. N. et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare 10, 2493 (2022).
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).
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).
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 (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 (2025).
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