Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Five tenets for advancing evidence-based precision medicine

Abstract

Precision medicine for complex diseases uses individual-level characteristics to improve prediction of risk, therapeutic response and prognosis. Many precision medicine studies leverage existing data types and analytic methods to reveal new insights; however, beyond oncology, there has been limited success in translating precision medicine research for complex diseases into clinical practice. Thus, there is a need to identify areas for improvement, particularly in translation-oriented analytical methods and study designs. In this perspective article, we outline five fundamental tenets to enhance the efficient clinical translation of precision medicine research. These tenets focus on addressing (1) heterogeneity in risk, response and prognosis; (2) signal robustness; (3) structured statistical benchmarking against key performance indicators; (4) precision trial designs; and (5) risks and benefits to individuals and society. Our intention is to promote clinically meaningful, reproducible, scalable and equitable health outcomes through precision medicine, beyond those possible through contemporary approaches.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Precision and contemporary approaches in medicine and healthcare.
Fig. 2: Responders and nonresponders in the SELECT trial.
Fig. 3: Benchmarking nested models with or without cluster allocations.
Fig. 4: Precision trial designs.

Similar content being viewed by others

References

  1. Sahni, N. R. & Carrus, B. Artificial intelligence in U.S. health care delivery. N. Engl. J. Med. 389, 348–358 (2023).

    Article  PubMed  Google Scholar 

  2. Lim, S. S. et al. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist. Nat. Med. 30, 1874–1881 (2024).

    Article  CAS  PubMed  Google Scholar 

  3. Tannock, I. F. et al. Importance of responder criteria for reporting health-related quality-of-life data in clinical trials for advanced cancer: recommendations of Common Sense Oncology and the European Organisation for Research and Treatment of Cancer. Lancet Oncol. 26, e499–e507 (2025).

    Article  PubMed  Google Scholar 

  4. Kent, D. M. et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement. Ann. Intern. Med. 172, 35–45 (2020).

    Article  PubMed  Google Scholar 

  5. Romanelli, R. J., Sudat, S., Huang, Q., Pressman, A. R. & Azar, K. Early weight loss and treatment response: data from a lifestyle change program in clinical practice. Am. J. Prev. Med. 58, 427–435 (2020).

    Article  PubMed  Google Scholar 

  6. Ard, J. D. et al. Differences in treatment response to a total diet replacement intervention versus a food-based intervention: a secondary analysis of the OPTIWIN trial. Obes. Sci. Pract. 6, 605–614 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Tronieri, J. S. et al. Anti-obesity medication for weight loss in early nonresponders to behavioral treatment: a randomized controlled trial. Nat. Med. 31, 1653–1660 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fujioka, K. et al. Early weight loss with liraglutide 3.0 mg predicts 1-year weight loss and is associated with improvements in clinical markers. Obesity (Silver Spring) 24, 2278–2288 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Morton, V. & Torgerson, D. J. Effect of regression to the mean on decision making in health care. BMJ 326, 1083–1084 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Snapinn, S. M. & Jiang, Q. Responder analyses and the assessment of a clinically relevant treatment effect. Trials 8, 31 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Nwanaji-Enwerem, J. C. & Mair, W. B. Redefining age-based screening and diagnostic guidelines: an opportunity for biological aging clocks in clinical medicine?. Lancet Healthy Longev. 3, e376–e377 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Argentieri, M. A. et al. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat. Med. 30, 2450–2460 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ikram, M. A. The use and misuse of ‘biological aging’ in health research. Nat. Med. 30, 3045 (2024).

    Article  CAS  PubMed  Google Scholar 

  14. Gajewski, B. J. & Dunton, N. Identifying individual changes in performance with composite quality indicators while accounting for regression to the mean. Med. Decis. Making 33, 396–406 (2013).

    Article  PubMed  Google Scholar 

  15. Hunter, D. J. & Holmes, C. Where medical statistics meets artificial intelligence. N. Engl. J. Med. 389, 1211–1219 (2023).

    Article  PubMed  Google Scholar 

  16. Chapfuwa, P., Li, C., Mehta, N., Carin, L. & Henao, R. Survival cluster analysis. In Proc. ACM Conference on Health, Inference, and Learning 60–68 (ACM, 2020).

  17. Mori, T. et al. Recognising, quantifying and accounting for classification uncertainty in type 2 diabetes subtypes. Diabetologia 68, 2139–2150 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wesolowska-Andersen, A. et al. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: an IMI DIRECT study. Cell Rep. Med. 3, 100477 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Asplund, O. et al. Comorbidities and mortality in subgroups of adults with diabetes with up to 14 years follow-up: a prospective cohort study in Sweden. Lancet Diabetes Endocrinol. 14, 29–40 (2026).

    Article  PubMed  Google Scholar 

  20. Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).

    Article  PubMed  Google Scholar 

  21. Dalmaijer, E. S., Nord, C. L. & Astle, D. E. Statistical power for cluster analysis. BMC Bioinformatics 23, 205 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Späth, H. Algorithm 39 clusterwise linear regression. Computing 22, 367–373 (1979).

    Article  Google Scholar 

  23. Fox, N. S. et al. iSubGen generates integrative disease subtypes by pairwise similarity assessment. Cell Rep. Methods 4, 100884 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Janakarajan, N., Larghero, G. & Rodríguez Martínez, M. Multi-modal clustering reveals event-free patient subgroup in colorectal cancer survival. NPJ Syst. Biol. Appl. 11, 86 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yan, X. et al. Noninvasive analysis of the sputum transcriptome discriminates clinical phenotypes of asthma. Am. J. Respir. Crit. Care Med. 191, 1116–1125 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, Z. et al. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat. Commun. 15, 354 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Arnedo, J. et al. Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies. Am. J. Psychiatry 172, 139–153 (2015).

    Article  PubMed  Google Scholar 

  28. Kim, H. et al. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease. Diabetologia 66, 495–507 (2023).

    Article  CAS  PubMed  Google Scholar 

  29. Mokry, M. et al. Transcriptomic-based clustering of human atherosclerotic plaques identifies subgroups with different underlying biology and clinical presentation. Nat. Cardiovasc. Res. 1, 1140–1155 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Eoli, A. et al. A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease. Sci. Rep. 14, 9642 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Vaura, F. et al. Multi-trait genetic analysis reveals clinically interpretable hypertension subtypes. Circ. Genom. Precis. Med. 15, e003583 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stamou, M. I. et al. Polycystic ovary syndrome physiologic pathways implicated through clustering of genetic loci. J. Clin. Endocrinol. Metab. 109, 968–977 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15, e1002654 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Smith, K. et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat. Med. 30, 1065–1074 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fortea, J. et al. APOE4 homozygosity represents a distinct genetic form of Alzheimer’s disease. Nat. Med. 30, 1284–1291 (2024).

    Article  CAS  PubMed  Google Scholar 

  36. Reeve, M. P. et al. Loss of CFHR5 function reduces the risk for age-related macular degeneration. Nat. Commun. 16, 5766 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Kozlitina, J. & Sookoian, S. Global epidemiological impact of PNPLA3 I148M on liver disease. Liver Int. 45, e16123 (2025).

    Article  PubMed  Google Scholar 

  38. Forrest, I. S. et al. Machine learning-based penetrance of genetic variants. Science 389, eadm7066 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803–810 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gravallese, E. M. & Firestein, G. S. Rheumatoid arthritis—common origins, divergent mechanisms. N. Engl. J. Med. 388, 529–542 (2023).

    Article  CAS  PubMed  Google Scholar 

  41. Boucly, A. et al. Clustering patients with pulmonary hypertension using the plasma proteome. Am. J. Respir. Crit. Care Med. 211, 1492–1503 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Chen, Y. et al. The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases. eBioMedicine 96, 104791 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kämpe, A. et al. Precision Omics Initiative Sweden (PROMISE) will integrate research with healthcare. Nat. Med. 31, 1730–1732 (2025).

    Article  PubMed  Google Scholar 

  44. Pan, W. et al. Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning. Nat. Commun. 16, 3797 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bair, E. & Tibshirani, R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2, e108 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Bowerman, K. L. et al. Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease. Nat. Commun. 11, 5886 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Suhre, K. et al. Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping. Nat. Commun. 15, 989 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun. 12, 6822 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kurgan, N., Kjærgaard Larsen, J. & Deshmukh, A. S. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 67, 783–797 (2024).

    Article  PubMed  Google Scholar 

  50. Benkirane, H. et al. Multimodal CustOmics: a unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology. PLoS Comput. Biol. 21, e1013012 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yoon, S.-H. & Nam, J.-W. Clustering malignant cell states using universally variable genes. Brief Bioinform. 25, bbad460 (2024).

    Article  Google Scholar 

  52. Kamiza, A. B. et al. Transferability of genetic risk scores in African populations. Nat. Med. 28, 1163–1166 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kulesa, A., Krzywinski, M., Blainey, P. & Altman, N. Sampling distributions and the bootstrap. Nat. Methods 12, 477–478 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lever, J., Krzywinski, M. & Altman, N. Model selection and overfitting. Nat. Methods 13, 703–704 (2016).

    Article  CAS  Google Scholar 

  55. Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, q902 (2024).

    Article  Google Scholar 

  56. MacKay, D. J. C. Information Theory, Inference and Learning Algorithms (Cambridge Univ. Press, 2003).

  57. Murphy, K. P. Probabilistic Machine Learning: An Introduction (MIT Press, 2022).

  58. Lin, J. Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37, 145–151 (1991).

    Article  Google Scholar 

  59. Van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S. & Vermunt, J. K. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Struct. Equ. Model. Multidiscip. J. 24, 451–467 (2017).

    Article  Google Scholar 

  60. Haq, I., Anwar, S., Shah, K., Khan, M. T. & Shah, S. A. Fuzzy logic based edge detection in smooth and noisy clinical images. PLoS ONE 10, e0138712 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Yamamoto, K. et al. Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: the KURAMA cohort study. PLoS ONE 19, e0302308 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Richter, M. et al. Generalizability of clinical prediction models in mental health. Mol. Psychiatry 30, 3632–3639 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Logeswaran, Y. & Oliver, D. It’s about time: why we need to consider temporal drift when developing and implementing clinical prediction models. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 10, 677–678 (2025).

    PubMed  Google Scholar 

  64. Foster, J. C., Taylor, J. M. G. & Ruberg, S. J. Subgroup identification from randomized clinical trial data. Stat. Med. 30, 2867–2880 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Pletcher, M. J. & McCulloch, C. E. The challenges of generating evidence to support precision medicine. JAMA Intern. Med. 177, 561–562 (2017).

    Article  PubMed  Google Scholar 

  66. Wang, Q. et al. A cluster-based cell-type deconvolution of spatial transcriptomic data. Nucleic Acids Res. 53, gkaf714 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Frommelt, F. et al. DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes. Nat. Methods 21, 635–647 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Coral, D. E. et al. Subclassification of obesity for precision prediction of cardiometabolic diseases. Nat. Med. 31, 534–543 (2025).

    Article  CAS  PubMed  Google Scholar 

  69. Buell, K. G. et al. Individualized treatment effects of oxygen targets in mechanically ventilated critically ill adults. JAMA 331, 1195–1204 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Harrell Jr, F. E. (ed.). Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis 181–217 (Springer International, 2015).

  71. Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7, 442–451 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Harrell, F. E. et al. Evaluating the yield of medical tests. JAMA 247, 2543–2546 (1982).

    Article  PubMed  Google Scholar 

  73. Hartman, N., Kim, S., He, K. & Kalbfleisch, J. D. Pitfalls of the concordance index for survival outcomes. Stat. Med. 42, 2179–2190 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Vickers, A. J., van Calster, B. & Steyerberg, E. W. A simple, step-by-step guide to interpreting decision curve analysis. Diagn. Progn. Res. 3, 18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Diaz-Uriarte, R. et al. Ten quick tips for biomarker discovery and validation analyses using machine learning. PLoS Comput. Biol. 18, e1010357 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Naci, H. et al. Design characteristics, risk of bias, and reporting of randomised controlled trials supporting approvals of cancer drugs by European Medicines Agency, 2014–16: cross sectional analysis. BMJ 366, l5221 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Kuss, O. et al. How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials. Diabetologia 66, 1622–1632 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bress, A. P. et al. Effect of intensive versus standard blood pressure treatment according to baseline prediabetes status: a post hoc analysis of a randomized trial. Diabetes Care 40, 1401–1408 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Dennis, J. M. et al. Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study. Lancet Digit. Health 4, e873–e883 (2022).

    Article  CAS  PubMed  Google Scholar 

  80. Shields, B. M. et al. Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study. Nat. Med. 29, 376–383 (2023).

    Article  CAS  PubMed  Google Scholar 

  81. Dennis, J. M. et al. A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study. Lancet 405, 701–714 (2025).

    Article  CAS  PubMed  Google Scholar 

  82. Lipkovich, I., Svensson, D., Ratitch, B. & Dmitrienko, A. Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data. Stat. Med. 43, 4388–4436 (2024).

    Article  PubMed  Google Scholar 

  83. Assmann, S. F., Pocock, S. J., Enos, L. E. & Kasten, L. E. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 355, 1064–1069 (2000).

    Article  CAS  PubMed  Google Scholar 

  84. Selker, H. P., Dulko, D., Greenblatt, D. J., Palm, M. & Trinquart, L. The use of N-of-1 trials to generate real-world evidence for optimal treatment of individuals and populations. J. Clin. Transl. Sci. 7, e203 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Selker, H. P. et al. A useful and sustainable role for N-of-1 trials in the healthcare ecosystem. Clin. Pharmacol. Ther. 112, 224–232 (2022).

    Article  PubMed  Google Scholar 

  86. Harris, P. A. et al. Enhancing multicenter trials with the trial innovation network’s initial consultation process. JAMA Netw. Open 8, e2512926 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Duan, X.-P. et al. New clinical trial design in precision medicine: discovery, development and direction. Signal Transduct. Target. Ther. 9, 57 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Superchi, C. et al. Study designs for clinical trials applied to personalised medicine: a scoping review. BMJ Open 12, e052926 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Redman, M. W. et al. Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer (Lung-MAP SWOG S1400): a biomarker-driven master protocol. Lancet Oncol. 21, 1589–1601 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Hyman, D. M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726–736 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Alexander, B. M. et al. Adaptive global innovative learning environment for glioblastoma: GBM AGILE. Clin. Cancer Res. 24, 737–743 (2018).

    Article  PubMed  Google Scholar 

  92. Antoniou, M., Jorgensen, A. L. & Kolamunnage-Dona, R. Biomarker-guided adaptive trial designs in phase II and phase III: a methodological review. PLoS ONE 11, e0149803 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Wason, J. M. S. et al. A Bayesian adaptive design for biomarker trials with linked treatments. Br. J. Cancer 113, 699–705 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Dwibedi, C. et al. Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology. Nat. Metab. 6, 50–60 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Shields, B. M. et al. Patient preference for second- and third-line therapies in type 2 diabetes: a prespecified secondary endpoint of the TriMaster study. Nat. Med. 29, 384–391 (2023).

    Article  CAS  PubMed  Google Scholar 

  96. Sundström, J. et al. Heterogeneity in blood pressure response to 4 antihypertensive drugs: a randomized clinical trial. JAMA 329, 1160–1169 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Atabaki-Pasdar, N. et al. Statistical power considerations in genotype-based recall randomized controlled trials. Sci. Rep. 6, 37307 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Slob, E. M. A. et al. Genotype-guided asthma treatment reduces exacerbations in children: meta-analysis of two randomized control trials. Allergy 80, 1006–1014 (2025).

    Article  CAS  PubMed  Google Scholar 

  99. Lancaster, T. et al. Proof-of-concept recall-by-genotype study of extremely low and high Alzheimer’s polygenic risk reveals autobiographical deficits and cingulate cortex correlates. Alzheimers Res. Ther. 15, 213 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Franks, P. W. & Timpson, N. J. Genotype-based recall studies in complex cardiometabolic traits. Circ. Genom. Precis. Med. 11, e001947 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Franks, P. W. & Sargent, J. L. Diabetes and obesity: leveraging heterogeneity for precision medicine. Eur. Heart J. 45, 5146–5155 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    Article  CAS  PubMed  Google Scholar 

  103. Chen, I. Y. et al. Ethical machine learning in healthcare. Ann. Rev. Biomed. Data Sci. 4, 123–144 (2021).

    Article  Google Scholar 

  104. Paulus, J. K. & Kent, D. M. Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities. NPJ Digit. Med. 3, 99 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Loftus, T. J. et al. Ideal algorithms in healthcare: explainable, dynamic, precise, autonomous, fair, and reproducible. PLoS Digit. Health 1, e0000006 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Shortliffe, E. H. & Sepúlveda, M. J. Clinical decision support in the era of artificial intelligence. JAMA 320, 2199–2200 (2018).

    Article  PubMed  Google Scholar 

  107. Kim, C., Gadgil, S. U. & Lee, S.-I. Transparency of medical artificial intelligence systems. Nat. Rev. Bioeng. 4, 11–29 (2026).

    Article  CAS  Google Scholar 

  108. Aboy, M., Minssen, T. & Vayena, E. Navigating the EU AI Act: implications for regulated digital medical products. NPJ Digit. Med. 7, 237 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  109. For trustworthy AI, keep the human in the loop. Nat. Med. 31, 3207 (2025).

  110. Greenwood, D. et al. Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential. iScience 25, 104480 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Raverdy, V. et al. Data-driven cluster analysis identifies distinct types of metabolic dysfunction-associated steatotic liver disease. Nat. Med. 30, 3624–3633 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Quintero, A. et al. Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data. BMJ Health Care Inform. 32, e101282 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Fabbrini, E. et al. Phase 1 trials of PNPLA3 siRNA in I148M homozygous patients with MAFLD. N. Engl. J. Med. 391, 475–476 (2024).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

A.J.D. is supported by the National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; K23 DK140643). M.S.U. is supported by NIH/NIDDK (U01DK140757 and U54DK118612) and the Doris Duke Foundation (grant 2022063). D.E.C., L.M.C., G.N.G., E.R.P. and P.W.F. were supported by grants from the European Commission (IMI SOPHIA, 875534), Swedish Research Council, Novo Nordisk Foundation, Vinnova and Swedish Foundation for Strategic Research (LUDC-IRC, 15-0067) and Exodiab (2009-1039). D.E.C. was supported by an EFSD/Lilly research grant.

Author information

Authors and Affiliations

Authors

Contributions

D.E.C. and P.W.F. conceived the core ideas described in this study. D.E.C., J.L.S. and P.W.F. wrote the initial draft of the manuscript. All authors contributed to the ideas described herein and reviewed and edited the manuscript before submission.

Corresponding authors

Correspondence to Daniel E. Coral or Paul W. Franks.

Ethics declarations

Competing interests

M.S.U. has consulting activity and research supported in collaboration with Novo Nordisk. J.L.S. has received consulting fees from the World Health Organization, University of Bergen, University of Heidelberg, Lund University, Karolinska University, Springer Nature, ABC Labs and European Diabetes Forum; travel support from University of Tubingen, University of Silensia, and European Association for Study of Diabetes; she is also founder of BabelFiskAB, which provides consulting services in global public health. P.W.F. has received consulting fees from Novo Nordisk and Zoe Ltd, and travel support and speaker fees from Menarini Group. P.W.F. has received research grants (paid to his institution) from Boehringer Ingelheim, Novo Nordisk, Medtronic, Pfizer and Lilly as part of the Innovative Health Initiative of the European Union. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Karen O’Leary, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coral, D.E., Sargent, J.L., Carrasco-Zanini, J. et al. Five tenets for advancing evidence-based precision medicine. Nat Med (2026). https://doi.org/10.1038/s41591-026-04309-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41591-026-04309-6

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing