{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T06:39:41Z","timestamp":1783838381178,"version":"3.55.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":41,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Advances in AI offer significant opportunities to enhance drug development. While several regulatory agencies have begun issuing guidance on AI adoption, its application to causal inference\u2014a critical piece to understand treatment effects and inform regulatory decisions\u2014remains limited. This paper reviews regulatory activities and examines statistical methodologies for AI-driven causal inference. We discuss key regulatory challenges and illustrate how AI adds value across diverse data sources and studies.<\/jats:p>","DOI":"10.1038\/s41746-026-02477-w","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T14:34:50Z","timestamp":1772202890000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Methodological and regulatory considerations for causal AI in drug development"],"prefix":"10.1038","volume":"9","author":[{"given":"Hana","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sky","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spencer","family":"Haupert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel K.","family":"Innes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Naumann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demissie","family":"Alemayehu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"van der Laan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"2477_CR1","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1002\/cpt.3152","volume":"115","author":"K Naik","year":"2024","unstructured":"Naik, K. et al. Current status and future directions: The application of artificial intelligence\/machine learning for precision medicine. Clin. Pharmacol. Ther. 115, 673\u2013686 (2024).","journal-title":"Clin. Pharmacol. Ther."},{"key":"2477_CR2","unstructured":"US Food and Drug Administration. Good machine learning practice for medical device development: Guiding principles. 2021. https:\/\/www.fda.gov\/media\/153486\/download, accessed 18 December 2025."},{"key":"2477_CR3","unstructured":"US Food and Drug Administration. Using artificial intelligence and machine learning in the development of drug and biological products. May 2023. Revised February 2025. https:\/\/www.fda.gov\/media\/167973\/download, accessed 18 December 2025."},{"key":"2477_CR4","unstructured":"US Food and Drug Administration. Artificial intelligence and medical products: How CBER, CDER, CDRH, and OCP are working together. 2024. Revised February 2025. https:\/\/www.fda.gov\/media\/177030\/download, accessed 18 December 2025."},{"key":"2477_CR5","unstructured":"US Food and Drug Administration. Considerations for the use of real-world data and real-world evidence to support regulatory decision-making for drug and biological products: guidance for industry. 2025. https:\/\/www.fda.gov\/media\/154714\/download, accessed 18 December 2025."},{"key":"2477_CR6","unstructured":"European Medicines Agency. Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. 2023. https:\/\/www.ema.europa.eu\/en\/documents\/scientific-guideline\/draft-reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf, accessed 18 December 2025."},{"key":"2477_CR7","unstructured":"European Medicines Agency. Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. 2024. https:\/\/www.ema.europa.eu\/en\/documents\/scientific-guideline\/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf, accessed 18 December 2025."},{"key":"2477_CR8","unstructured":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. General principles for model-informed drug development M15. 2024. https:\/\/www.fda.gov\/media\/184747\/download, accessed 18 December 2025."},{"key":"2477_CR9","unstructured":"US Food and Drug Administration. Real-world data: Assessing electronic health records and medical claims data to support regulatory decision-making for drug and biological products. 2024. https:\/\/www.fda.gov\/media\/152503\/download, accessed 18 December 2025."},{"key":"2477_CR10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","volume":"70","author":"PR Rosenbaum","year":"1983","unstructured":"Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41\u201355 (1983).","journal-title":"Biometrika"},{"key":"2477_CR11","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1097\/00001648-200009000-00011","volume":"11","author":"JM Robins","year":"2000","unstructured":"Robins, J. M., Hern\u00e1n, M. A. & Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550\u2013560 (2000).","journal-title":"Epidemiology"},{"key":"2477_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/09-STS313","volume":"25","author":"EA Stuart","year":"2010","unstructured":"Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1\u201321 (2010).","journal-title":"Stat. Sci."},{"key":"2477_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1093\/biomet\/asn055","volume":"96","author":"RK Crump","year":"2009","unstructured":"Crump, R. K., Hotz, V. J., Imbens, G. W. & Mitnik, O. A. Dealing with limited overlap in estimation of average treatment effects. Biometrika 96, 187\u2013199 (2009).","journal-title":"Biometrika"},{"key":"2477_CR14","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1093\/aje\/kwq198","volume":"172","author":"T St\u00fcrmer","year":"2010","unstructured":"St\u00fcrmer, T., Rothman, K. J., Avorn, J. & Glynn, R. J. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution\u2014a simulation study. Am. J. Epidemiol. 172, 843\u2013854 (2010).","journal-title":"Am. J. Epidemiol."},{"key":"2477_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1093\/pan\/mpr025","volume":"20","author":"J Hainmueller","year":"2012","unstructured":"Hainmueller, J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Political Anal. 20, 25\u201346 (2012).","journal-title":"Political Anal."},{"key":"2477_CR16","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1080\/01621459.2016.1260466","volume":"113","author":"F Li","year":"2018","unstructured":"Li, F., Morgan, K. L. & Zaslavsky, A. M. Balancing covariates via propensity score weighting. J. Am. Stat. Assoc. 113, 390\u2013400 (2018).","journal-title":"J. Am. Stat. Assoc."},{"key":"2477_CR17","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1002\/pds.5639","volume":"32","author":"PC Austin","year":"2023","unstructured":"Austin, P. C. Differences in target estimands between different propensity score-based weights. Pharmacoepidemiol. Drug Saf. 32, 1103\u20131112 (2023).","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"2477_CR18","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1080\/01621459.1984.10478078","volume":"79","author":"PR Rosenbaum","year":"1984","unstructured":"Rosenbaum, P. R. & Rubin, D. B. Reducing bias in observational studies using subclassification on the propensity score. J. Am. Stat. Assoc. 79, 516\u2013524 (1984).","journal-title":"J. Am. Stat. Assoc."},{"key":"2477_CR19","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1002\/pds.1673","volume":"17","author":"PC Austin","year":"2008","unstructured":"Austin, P. C. Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score. Pharmacoepidemiol. Drug Saf. 17, 1202\u20131217 (2008).","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"2477_CR20","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1037\/1082-989X.9.4.403","volume":"9","author":"DF McCaffrey","year":"2004","unstructured":"McCaffrey, D. F., Ridgeway, G. & Morral, A. R. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol. Methods 9, 403\u2013425 (2004).","journal-title":"Psychol. Methods"},{"key":"2477_CR21","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1002\/pds.1555","volume":"17","author":"S Setoguchi","year":"2008","unstructured":"Setoguchi, S., Schneeweiss, S., Brookhart, M. A., Glynn, R. J. & Cook, E. F. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiol. Drug Saf. 17, 546\u2013555 (2008).","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"2477_CR22","first-page":"448","volume":"62","author":"D Westreich","year":"2009","unstructured":"Westreich, D., Lessler, J. & Funk, M. J. Propensity score estimation and classification methods: alternatives to logistic regression. J. Clin. Epidemiol. 62, 448\u2013454 (2009).","journal-title":"J. Clin. Epidemiol."},{"key":"2477_CR23","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1016\/j.jclinepi.2009.11.020","volume":"63","author":"D Westreich","year":"2010","unstructured":"Westreich, D., Lessler, J. & Funk, M. J. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J. Clin. Epidemiol. 63, 826\u2013833 (2010).","journal-title":"J. Clin. Epidemiol."},{"key":"2477_CR24","doi-asserted-by":"publisher","first-page":"5016","DOI":"10.1002\/sim.9551","volume":"41","author":"TH Chang","year":"2022","unstructured":"Chang, T. H., Nguyen, T. Q., Lee, Y., Jackson, J. W. & Stuart, E. A. Flexible propensity score estimation strategies for clustered data in observational studies. Stat. Med. 41, 5016\u20135032 (2022).","journal-title":"Stat. Med."},{"key":"2477_CR25","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1002\/sim.3782","volume":"29","author":"BK Lee","year":"2010","unstructured":"Lee, B. K., Lessler, J. & Stuart, E. A. Improving propensity score weighting using machine learning. Stat. Med. 29, 337\u2013346 (2010).","journal-title":"Stat. Med."},{"key":"2477_CR26","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1214\/18-STS667","volume":"34","author":"V Dorie","year":"2019","unstructured":"Dorie, V., Hill, J., Shalit, U., Scott, M. & Cervone, D. Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition. Stat. Sci. 34, 43\u201368 (2019).","journal-title":"Stat. Sci."},{"key":"2477_CR27","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1016\/0270-0255(86)90088-6","volume":"7","author":"JM Robins","year":"1986","unstructured":"Robins, J. M. A new approach to causal inference in mortality studies with a sustained exposure period\u2014application to control of the healthy worker survivor effect. Math. Model. 7, 1393\u20131512 (1986).","journal-title":"Math. Model."},{"key":"2477_CR28","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1111\/j.1541-0420.2005.00377.x","volume":"61","author":"H Bang","year":"2005","unstructured":"Bang, H. & Robins, J. M. Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962\u2013973 (2005).","journal-title":"Biometrics"},{"key":"2477_CR29","doi-asserted-by":"crossref","unstructured":"van der Laan, M. J. & Robins, J. M. Unified Methods for Censored Longitudinal Data and Causality (Springer, 2003).","DOI":"10.1007\/978-0-387-21700-0"},{"key":"2477_CR30","unstructured":"Tsiatis, A. A. Semiparametric Theory and Missing Data (Springer, 2006)."},{"key":"2477_CR31","doi-asserted-by":"crossref","unstructured":"Coyle, J., & van der Laan, M. J. Targeted bootstrap. In Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies Book 523\u2013539. (Springer, 2018).","DOI":"10.1007\/978-3-319-65304-4_28"},{"key":"2477_CR32","unstructured":"Laan, M. J. & Robins, J. M. Unified Methods for Censored Longitudinal Data and Causality (Springer 2003)."},{"key":"2477_CR33","first-page":"11","volume":"2","author":"MJ van der Laan","year":"2006","unstructured":"van der Laan, M. J. & Rubin, D. Targeted maximum likelihood learning. Int. J. Biostat. 2, 11 (2006).","journal-title":"Int. J. Biostat."},{"key":"2477_CR34","doi-asserted-by":"crossref","unstructured":"van der Laan, M. J. & Rose, S. Targeted Learning: Causal Inference for Observational and Experimental Data (Springer, 2011).","DOI":"10.1007\/978-1-4419-9782-1"},{"key":"2477_CR35","doi-asserted-by":"crossref","unstructured":"van der Laan, M. J. & Rose, S. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer, 2018).","DOI":"10.1007\/978-3-319-65304-4"},{"key":"2477_CR36","doi-asserted-by":"crossref","unstructured":"Hirano, K. & Imbens, G. W. The propensity score with continuous treatments. In Applied Bayesian modeling and causal inference from incomplete-data perspectives: An essential journey with Donald Rubin\u2019s Statistical Family (eds D. B. Rubin, A. Gelman, & X.-L. Meng) 73\u201384 (Wiley, 2004).","DOI":"10.1002\/0470090456.ch7"},{"key":"2477_CR37","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01621459.2013.869498","volume":"109","author":"CM Zigler","year":"2014","unstructured":"Zigler, C. M. & Dominici, F. Uncertainty in propensity score estimation: Bayesian methods for variable selection and model-averaged causal effects. J. Am. Stat. Assoc. 109, 95\u2013107 (2014).","journal-title":"J. Am. Stat. Assoc."},{"key":"2477_CR38","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1198\/jcgs.2010.08162","volume":"20","author":"JL Hill","year":"2011","unstructured":"Hill, J. L. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20, 217\u2013240 (2011).","journal-title":"J. Comput. Graph. Stat."},{"key":"2477_CR39","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1080\/07350015.2016.1172013","volume":"34","author":"M Taddy","year":"2016","unstructured":"Taddy, M., Gardner, M., Chen, L. & Draper, D. A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation. J. Bus. Econ. Stat. 34, 661\u2013672 (2016).","journal-title":"J. Bus. Econ. Stat."},{"key":"2477_CR40","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1080\/01621459.2017.1319839","volume":"113","author":"S Wager","year":"2018","unstructured":"Wager, S. & Athey, S. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113, 1228\u20131242 (2018).","journal-title":"J. Am. Stat. Assoc."},{"key":"2477_CR41","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1214\/19-BA1195","volume":"15","author":"PR Hahn","year":"2020","unstructured":"Hahn, P. R., Murray, J. S. & Carvalho, C. M. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion). Bayesian Anal. 15, 965\u20131056 (2020).","journal-title":"Bayesian Anal."},{"key":"2477_CR42","unstructured":"Babasaki, K., Sugasawa, S., Takanashi, K., & McAlinn, K. Ensemble Doubly Robust Bayesian Inference via Regression Synthesis. arXiv preprint arXiv:2409.06288. (2024)."},{"key":"2477_CR43","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1111\/jep.13542","volume":"27","author":"F De Pretis","year":"2021","unstructured":"De Pretis, F., Landes, J. & Peden, W. Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation. J. Eval. Clin. Prac. 27, 504\u2013512 (2021).","journal-title":"J. Eval. Clin. Prac."},{"key":"2477_CR44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253057","volume":"16","author":"F De Pretis","year":"2021","unstructured":"De Pretis, F. & Landes, J. EA3: a softmax algorithm for evidence appraisal aggregation. PLoS One 16, e0253057 (2021).","journal-title":"PLoS One"},{"key":"2477_CR45","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2001).","DOI":"10.1007\/978-0-387-21606-5"},{"key":"2477_CR46","unstructured":"Wind, D. K. & Winther, O. Model selection in data analysis competitions. In 21st European Conference on Artificial Intelligence 55\u201360 (2014)."},{"key":"2477_CR47","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2202\/1544-6115.1309","volume":"6","author":"MJ van der Laan","year":"2007","unstructured":"van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6, 25 (2007).","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"2477_CR48","unstructured":"van der Laan, M. J. & Dudoit, S. Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: finite sample oracle inequalities and examples. UC Berkeley Division of Biostatistics Working Paper Series, Working Paper 130 (2003)."},{"key":"2477_CR49","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1524\/stnd.2006.24.3.351","volume":"24","author":"AW van der Vaart","year":"2006","unstructured":"van der Vaart, A. W., Dudoit, S. & van der Laan, M. J. Oracle inequalities for multi-fold cross validation. Stat. Decis. 24, 351\u2013371 (2006).","journal-title":"Stat. Decis."},{"key":"2477_CR50","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1080\/19466315.2022.2116104","volume":"15","author":"S Gruber","year":"2023","unstructured":"Gruber, S., Lee, H., Phillips, R., Ho, M. & van der Laan, M. J. Developing a targeted learning-based statistical analysis plan. Stat. Biopharm. Res. 15, 468\u2013475 (2023).","journal-title":"Stat. Biopharm. Res."},{"key":"2477_CR51","doi-asserted-by":"publisher","first-page":"2446","DOI":"10.1001\/jama.2022.21383","volume":"328","author":"MA Hern\u00e1n","year":"2022","unstructured":"Hern\u00e1n, M. A., Wang, W. & Leaf, D. E. Target trial emulation: a framework for causal inference from observational data. JAMA 328, 2446\u20132447 (2022).","journal-title":"JAMA"},{"key":"2477_CR52","doi-asserted-by":"publisher","DOI":"10.1017\/cts.2023.635","volume":"7","author":"LE Dang","year":"2023","unstructured":"Dang, L. E. et al. A causal roadmap for generating high-quality real-world evidence. J. Clin. Transl. Sci. 7, e212 (2023).","journal-title":"J. Clin. Transl. Sci."},{"key":"2477_CR53","unstructured":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E9(R1) Statistical principles for clinical trials: Addendum: Estimands and sensitivity analysis in clinical trials. 2021. https:\/\/www.fda.gov\/media\/148473\/download, accessed 18 December 2025."},{"key":"2477_CR54","unstructured":"US Food and Drug Administration. Real-world data: Assessing registries to support regulatory decision-making for drug and biological products. 2023. https:\/\/www.fda.gov\/media\/154449\/download, accessed 18 December 2025."},{"key":"2477_CR55","unstructured":"US Food and Drug Administration. Framework for FDA\u2019s real-world evidence program. 2018. https:\/\/www.fda.gov\/media\/120060\/download?attachment, accessed 18 December 2025."},{"key":"2477_CR56","unstructured":"US Food and Drug Administration. Rare Diseases: Considerations for the Development of Drugs and Biological Products. 2023. https:\/\/www.fda.gov\/media\/119757\/download, accessed 18 December 2025."},{"key":"2477_CR57","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1080\/19466315.2022.2108135","volume":"15","author":"D Zhang","year":"2023","unstructured":"Zhang, D. et al. The use of machine learning in regulatory drug safety evaluation. Stat. Biopharm. Res. 15, 519\u2013523 (2023).","journal-title":"Stat. Biopharm. Res."},{"key":"2477_CR58","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s40264-022-01157-4","volume":"45","author":"R Ball","year":"2022","unstructured":"Ball, R. & Dal Pan, G. \u201cArtificial intelligence\u201d for pharmacovigilance: ready for prime time? Drug Saf. 45, 429\u2013438 (2022).","journal-title":"Drug Saf."},{"key":"2477_CR59","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s41746-021-00542-0","volume":"4","author":"RJ Desai","year":"2021","unstructured":"Desai, R. J. et al. Broadening the reach of the FDA Sentinel system: a roadmap for integrating electronic health record data in a causal analysis framework. npj Digit. Med. 4, 170 (2021).","journal-title":"npj Digit. Med."},{"key":"2477_CR60","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1093\/aje\/kwae023","volume":"193","author":"R Wyss","year":"2024","unstructured":"Wyss, R. et al. Targeted learning with an undersmoothed LASSO propensity score model for large-scale covariate adjustment in health-care database studies. Am. J. Epidemiol. 193, 1632\u20131640 (2024).","journal-title":"Am. J. Epidemiol."},{"key":"2477_CR61","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.tips.2022.03.009","volume":"43","author":"F De Pretis","year":"2022","unstructured":"De Pretis, F., van Gils, M. & Forsberg, M. M. A smart hospital-driven approach to precision pharmacovigilance. Trends Pharmacol. Sci. 43, 473\u2013481 (2022).","journal-title":"Trends Pharmacol. Sci."},{"key":"2477_CR62","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1177\/09246479251365094","volume":"36","author":"F De Pretis","year":"2025","unstructured":"De Pretis, F. et al. Innovative approaches to collecting, aggregating, and analyzing adverse drug events in smart hospitals. Int. J. Risk Saf. Med. 36, 289\u2013301 (2025).","journal-title":"Int. J. Risk Saf. Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02477-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02477-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02477-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T10:05:42Z","timestamp":1775729142000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02477-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2477"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02477-w","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]},"assertion":[{"value":"30 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"295"}}