{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T07:05:54Z","timestamp":1776755154498,"version":"3.51.2"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Health Systems Research Institute (HSRI), Thailand","award":["HSRI.67-127"],"award-info":[{"award-number":["HSRI.67-127"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence has empowered precision medicine (AI-PM) to transform healthcare. This study synthesized available evidence on the cost-effectiveness of AI-PM. We systematically searched five major databases for economic evaluations of AI-PM, extracted data, and assessed risk-of-bias using the Bias in Economic Evaluation (ECOBIAS) checklist. For cost-utility analyses, the value-for-money was quantitatively summarized, and regression analyses incorporating machine learning were conducted to explore value heterogeneity. Forty-eight economic evaluations were included, of which 31 were cost-utility analyses. Although risk-of-bias assessment indicated potential systematic optimism, AI-PM was cost-saving or cost-effective in 89% of base-case analyses, with incremental cost-effectiveness ratios ranging from dominant to $129,174\/quality-adjusted life-year (QALY). Interquartile ranges of incremental costs (\u2212$259 to $28), QALY gains (0.001\u20130.019), and net monetary benefits (NMB; $18 to $986 at a willingness-to-pay threshold equal to one-time per-capita GDP) indicated modest health gains at minimal additional costs, and likely high value heterogeneity. Modeling choices and system-level factors were identified as essential sources of heterogeneity in estimated NMBs. Additional value assessment revealed low adaptability and underreported key value factors, leaving significant uncertainties in AI-PM adoption.<\/jats:p>","DOI":"10.1038\/s41746-025-02259-w","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T16:43:00Z","timestamp":1766594580000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The value for money of artificial intelligence-empowered precision medicine: a systematic review and regression analysis"],"prefix":"10.1038","volume":"9","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Ziwei","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yot","family":"Teerawattananon","sequence":"additional","affiliation":[]},{"given":"Katika","family":"Akksilp","sequence":"additional","affiliation":[]},{"given":"Alec","family":"Morton","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Thittaya","family":"Prapinvanich","sequence":"additional","affiliation":[]},{"given":"Thamonwan","family":"Dulsamphan","sequence":"additional","affiliation":[]},{"given":"Wenjia","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"2259_CR1","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6939-14-55","volume":"14","author":"S Schleidgen","year":"2013","unstructured":"Schleidgen, S., Klingler, C., Bertram, T., Rogowski, W. H. & Marckmann, G. What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med. Ethics 14, 55 (2013).","journal-title":"BMC Med. Ethics"},{"key":"2259_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaut.2020.102405","volume":"110","author":"D Aletaha","year":"2020","unstructured":"Aletaha, D. Precision medicine and management of rheumatoid arthritis. J. Autoimmun. 110, 102405 (2020).","journal-title":"J. Autoimmun."},{"key":"2259_CR3","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1111\/cts.12884","volume":"14","author":"KB Johnson","year":"2021","unstructured":"Johnson, K. B. et al. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 14, 86\u201393 (2021).","journal-title":"Clin. Transl. Sci."},{"key":"2259_CR4","doi-asserted-by":"publisher","first-page":"106020","DOI":"10.1016\/j.compbiomed.2022.106020","volume":"149","author":"R Thirunavukarasu","year":"2022","unstructured":"Thirunavukarasu, R. et al. Towards computational solutions for precision medicine based big data healthcare system using deep learning models: a review. Comput. Biol. Med. 149, 106020 (2022).","journal-title":"Comput. Biol. Med."},{"key":"2259_CR5","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","volume":"15","author":"I Kavakiotis","year":"2017","unstructured":"Kavakiotis, I. et al. Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15, 104\u2013116 (2017).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"2259_CR6","doi-asserted-by":"crossref","unstructured":"Lin, E., Lin, C. H. & Lane, H. Y. Precision psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int. J. Mol. Sci. 21, 969 (2020).","DOI":"10.3390\/ijms21030969"},{"key":"2259_CR7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","volume":"6","author":"T Davenport","year":"2019","unstructured":"Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Health. J. 6, 94\u201398 (2019).","journal-title":"Future Health. J."},{"key":"2259_CR8","unstructured":"HeartFlow: unlocking the power of coronary CTA. HeartFlow. Available at: https:\/\/www.heartflow.com\/heartflow-ffrct-analysis\/."},{"key":"2259_CR9","unstructured":"About SELENA+. Synapxe. Available at: https:\/\/www.synapxe.sg\/healthtech\/health-ai\/selena."},{"key":"2259_CR10","unstructured":"Empowering radiologists for better outcomes in women\u2019s health. CureMetrix. Available at: https:\/\/curemetrix.com\/."},{"key":"2259_CR11","unstructured":"An end-to-end adaptive AI-assisted 3H care (A3C) system. AI Singapore. Available at: https:\/\/aisingapore.org\/an-end-to-end-adaptive-ai-assisted-3h-care-a3c-system\/."},{"key":"2259_CR12","unstructured":"Precision medicine: finding cancer treatments. CureMatch. Available at: https:\/\/www.curematch.com\/."},{"key":"2259_CR13","unstructured":"Start, run, and grow the impact of your genetics program. CancerIQ. Available at: https:\/\/www.canceriq.com\/oncology."},{"key":"2259_CR14","unstructured":"Effective cancer therapy based on tumor deep molecular profiling. Oncobox. Available at: https:\/\/oncobox.com\/."},{"key":"2259_CR15","unstructured":"DBLG1: smart technology to manage Type 1 diabetes. Diabeloop. Available at: https:\/\/www.dbl-diabetes.com\/dblg1-system."},{"key":"2259_CR16","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1080\/13696998.2023.2285186","volume":"26","author":"AZ Al Meslamani","year":"2023","unstructured":"Al Meslamani, A. Z. Beyond implementation: the long-term economic impact of AI in healthcare. J. Med. Econ. 26, 1566\u20131569 (2023).","journal-title":"J. Med. Econ."},{"key":"2259_CR17","doi-asserted-by":"publisher","first-page":"e25759","DOI":"10.2196\/25759","volume":"23","author":"J Yin","year":"2021","unstructured":"Yin, J., Ngiam, K. Y. & Teo, H. H. Role of artificial intelligence applications in real-life clinical practice: systematic review. J. Med. Internet Res. 23, e25759 (2021).","journal-title":"J. Med. Internet Res."},{"key":"2259_CR18","doi-asserted-by":"crossref","unstructured":"Garber, A. M. & Sculpher, M. J. Handbook of Health Economics, Vol. 2, 471\u2013497 (Elsevier, 2011).","DOI":"10.1016\/B978-0-444-53592-4.00008-6"},{"key":"2259_CR19","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1017\/S0266462307080051","volume":"24","author":"JB Pietzsch","year":"2008","unstructured":"Pietzsch, J. B. & Pate-Cornell, M. E. Early technology assessment of new medical devices. Int. J. Technol. Assess. Health Care 24, 36\u201344 (2008).","journal-title":"Int. J. Technol. Assess. Health Care"},{"key":"2259_CR20","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s40273-017-0509-1","volume":"35","author":"IJ MJ","year":"2017","unstructured":"MJ, I. J., Koffijberg, H., Fenwick, E. & Krahn, M. Emerging use of early health technology assessment in medical product development: a scoping review of the literature. Pharmacoeconomics 35, 727\u2013740 (2017).","journal-title":"Pharmacoeconomics"},{"key":"2259_CR21","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.jval.2021.11.1362","volume":"25","author":"MM Voets","year":"2022","unstructured":"Voets, M. M., Veltman, J., Slump, C. H., Siesling, S. & Koffijberg, H. Systematic review of health economic evaluations focused on artificial intelligence in healthcare: the tortoise and the cheetah. Value Health 25, 340\u2013349 (2022).","journal-title":"Value Health"},{"key":"2259_CR22","doi-asserted-by":"publisher","first-page":"1220950","DOI":"10.3389\/fphar.2023.1220950","volume":"14","author":"J Vithlani","year":"2023","unstructured":"Vithlani, J., Hawksworth, C., Elvidge, J., Ayiku, L. & Dawoud, D. Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting. Front. Pharm. 14, 1220950 (2023).","journal-title":"Front. Pharm."},{"key":"2259_CR23","doi-asserted-by":"publisher","first-page":"e436","DOI":"10.1016\/S2589-7500(22)00042-5","volume":"4","author":"M Areia","year":"2022","unstructured":"Areia, M. et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health 4, e436\u2013e444 (2022).","journal-title":"Lancet Digit Health"},{"key":"2259_CR24","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.2217\/cer-2021-0115","volume":"10","author":"PJ Mallow","year":"2021","unstructured":"Mallow, P. J. & Belk, K. W. Cost-utility analysis of single nucleotide polymorphism panel-based machine learning algorithm to predict risk of opioid use disorder. J. Comp. Eff. Res. 10, 1349\u20131361 (2021).","journal-title":"J. Comp. Eff. Res."},{"key":"2259_CR25","doi-asserted-by":"publisher","first-page":"e0254950","DOI":"10.1371\/journal.pone.0254950","volume":"16","author":"J Salcedo","year":"2021","unstructured":"Salcedo, J. et al. Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: a modeling study. PLoS ONE 16, e0254950 (2021).","journal-title":"PLoS ONE"},{"key":"2259_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-022-09613-1","volume":"22","author":"S Mital","year":"2022","unstructured":"Mital, S. & Nguyen, H. V. Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening. BMC Cancer 22, 501 (2022).","journal-title":"BMC Cancer"},{"key":"2259_CR27","first-page":"5471","volume":"60","author":"Y Xie","year":"2019","unstructured":"Xie, Y. et al. Cost-effectiveness analysis of an artificial intelligence-assisted deep learning system implemented in the national tele-medicine diabetic retinopathy screening in Singapore. Investig. Ophthalmol. Vis. Sci. 60, 5471\u20135471 (2019).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"2259_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1151504","volume":"11","author":"W Chen","year":"2023","unstructured":"Chen, W. et al. Mapping the value for money of precision medicine: a systematic literature review and meta-analysis. Front. Public Health 11, 1151504 (2023).","journal-title":"Front. Public Health"},{"key":"2259_CR29","doi-asserted-by":"publisher","first-page":"109391","DOI":"10.1016\/j.compbiomed.2024.109391","volume":"184","author":"A Uwimana","year":"2025","unstructured":"Uwimana, A., Gnecco, G. & Riccaboni, M. Artificial intelligence for breast cancer detection and its health technology assessment: a scoping review. Comput. Biol. Med. 184, 109391 (2025).","journal-title":"Comput. Biol. Med."},{"key":"2259_CR30","doi-asserted-by":"crossref","unstructured":"Kolasa, K. & Kozinski, G. How to value digital health interventions? A systematic literature review. Int. J. Environ. Res. Public Health 17, 2119 (2020).","DOI":"10.3390\/ijerph17062119"},{"key":"2259_CR31","doi-asserted-by":"publisher","first-page":"e48392","DOI":"10.2196\/48392","volume":"25","author":"B Mesko","year":"2023","unstructured":"Mesko, B. The ChatGPT (generative artificial intelligence) revolution has made artificial intelligence approachable for medical professionals. J. Med. Internet Res. 25, e48392 (2023).","journal-title":"J. Med. Internet Res."},{"key":"2259_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01402-3","volume":"8","author":"M De Domenico","year":"2025","unstructured":"De Domenico, M. et al. Challenges and opportunities for digital twins in precision medicine from a complex systems perspective. npj Digit. Med. 8, 37 (2025).","journal-title":"npj Digit. Med."},{"key":"2259_CR33","doi-asserted-by":"crossref","unstructured":"Shmatko, A. et al. Learning the natural history of human disease with generative transformers. Nature, 647, 248\u2013256 (2025).","DOI":"10.1038\/s41586-025-09529-3"},{"key":"2259_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01776-y","volume":"8","author":"SJ Adams","year":"2025","unstructured":"Adams, S. J., Acosta, J. N. & Rajpurkar, P. How generative AI voice agents will transform medicine. npj Digit. Med. 8, 353 (2025).","journal-title":"npj Digit. Med."},{"key":"2259_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01147-z","volume":"7","author":"R Mathias","year":"2024","unstructured":"Mathias, R., McCulloch, P., Chalkidou, A. & Gilbert, S. Digital health technologies need regulation and reimbursement that enable flexible interactions and groupings. npj Digit. Med. 7, 148 (2024).","journal-title":"npj Digit. Med."},{"key":"2259_CR36","doi-asserted-by":"publisher","unstructured":"Heydari, A. A. et al. The anatomy of a personal health agent. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2508.20148 (2025).","DOI":"10.48550\/arXiv.2508.20148"},{"key":"2259_CR37","doi-asserted-by":"publisher","first-page":"861","DOI":"10.2165\/11312720-000000000-00000","volume":"27","author":"PJ Neumann","year":"2009","unstructured":"Neumann, P. J., Fang, C. H. & Cohen, J. T. 30 years of pharmaceutical cost-utility analyses: growth, diversity and methodological improvement. Pharmacoeconomics 27, 861\u2013872 (2009).","journal-title":"Pharmacoeconomics"},{"key":"2259_CR38","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1007\/s40273-020-00942-2","volume":"38","author":"DD Kim","year":"2020","unstructured":"Kim, D. D. et al. Perspective and costing in cost-effectiveness analysis, 1974-2018. Pharmacoeconomics 38, 1135\u20131145 (2020).","journal-title":"Pharmacoeconomics"},{"key":"2259_CR39","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1136\/bmj.316.7143.1529","volume":"316","author":"S Byford","year":"1998","unstructured":"Byford, S. & Raftery, J. Perspectives in economic evaluation. BMJ 316, 1529\u20131530 (1998).","journal-title":"BMJ"},{"key":"2259_CR40","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s12962-024-00552-1","volume":"22","author":"M Sittimart","year":"2024","unstructured":"Sittimart, M. et al. An overview of the perspectives used in health economic evaluations. Cost. Eff. Resour. Alloc. 22, 41 (2024).","journal-title":"Cost. Eff. Resour. Alloc."},{"key":"2259_CR41","doi-asserted-by":"publisher","first-page":"e0260808","DOI":"10.1371\/journal.pone.0260808","volume":"16","author":"KL Rosettie","year":"2021","unstructured":"Rosettie, K. L. et al. Cost-effectiveness of HPV vaccination in 195 countries: a meta-regression analysis. PLoS ONE 16, e0260808 (2021).","journal-title":"PLoS ONE"},{"key":"2259_CR42","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1001\/jama.2019.1153","volume":"321","author":"A Basu","year":"2019","unstructured":"Basu, A. & Maciejewski, M. L. Choosing a time horizon in cost and cost-effectiveness analyses. JAMA 321, 1096\u20131097 (2019).","journal-title":"JAMA"},{"key":"2259_CR43","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1093\/heapol\/czaa073","volume":"35","author":"M Haacker","year":"2020","unstructured":"Haacker, M., Hallett, T. B. & Atun, R. On time horizons in health economic evaluations. Health Policy Plan. 35, 1237\u20131243 (2020).","journal-title":"Health Policy Plan."},{"key":"2259_CR44","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1007\/s00134-018-5293-7","volume":"44","author":"A Lundh","year":"2018","unstructured":"Lundh, A., Lexchin, J., Mintzes, B., Schroll, J. B. & Bero, L. Industry sponsorship and research outcome: systematic review with meta-analysis. Intensive Care Med. 44, 1603\u20131612 (2018).","journal-title":"Intensive Care Med."},{"key":"2259_CR45","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1016\/j.jval.2022.01.006","volume":"25","author":"H Vellekoop","year":"2022","unstructured":"Vellekoop, H. et al. The net benefit of personalized medicine: a systematic literature review and regression analysis. Value Health 25, 1428\u20131438 (2022).","journal-title":"Value Health"},{"key":"2259_CR46","doi-asserted-by":"crossref","unstructured":"Elvidge, J. et al. Consolidated health economic evaluation reporting standards for interventions that use artificial intelligence (CHEERS-AI). Value Health 27, 1196\u20131205 (2024).","DOI":"10.1016\/j.jval.2024.05.006"},{"key":"2259_CR47","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1017\/S0266462317000484","volume":"33","author":"P Wahlster","year":"2017","unstructured":"Wahlster, P. et al. An integrated perspective on the assessment of technologies: Integrate-Hta. Int. J. Technol. Assess. Health Care 33, 544\u2013551 (2017).","journal-title":"Int. J. Technol. Assess. Health Care"},{"key":"2259_CR48","doi-asserted-by":"publisher","first-page":"e17707","DOI":"10.2196\/17707","volume":"22","author":"H Alami","year":"2020","unstructured":"Alami, H. et al. Artificial intelligence and health technology assessment: anticipating a new level of complexity. J. Med. Internet Res. 22, e17707 (2020).","journal-title":"J. Med. Internet Res."},{"key":"2259_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2024.111783","volume":"181","author":"S Sridharan","year":"2024","unstructured":"Sridharan, S. et al. Real-World evaluation of an AI triaging system for chest X-rays: a prospective clinical study. Eur. J. Radio. 181, 111783 (2024).","journal-title":"Eur. J. Radio."},{"key":"2259_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2022.104080","volume":"119","author":"F Schwendicke","year":"2022","unstructured":"Schwendicke, F. et al. Cost-effectiveness of AI for caries detection: randomized trial. J. Dent. 119, 104080 (2022).","journal-title":"J. Dent."},{"key":"2259_CR51","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1177\/0022034520972335","volume":"100","author":"F Schwendicke","year":"2021","unstructured":"Schwendicke, F. et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J. Dent. Res. 100, 369\u2013376 (2021).","journal-title":"J. Dent. Res."},{"key":"2259_CR52","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1177\/00220345221113756","volume":"101","author":"F Schwendicke","year":"2022","unstructured":"Schwendicke, F. et al. Artificial intelligence for caries detection: value of data and information. J. Dent. Res. 101, 1350\u20131356 (2022).","journal-title":"J. Dent. Res."},{"key":"2259_CR53","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.0269","volume":"5","author":"J Gomez Rossi","year":"2022","unstructured":"Gomez Rossi, J., Rojas-Perilla, N., Krois, J. & Schwendicke, F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Netw. Open 5, e220269 (2022).","journal-title":"JAMA Netw. Open"},{"key":"2259_CR54","doi-asserted-by":"publisher","first-page":"e1159","DOI":"10.1016\/S2214-109X(24)00181-5","volume":"12","author":"F Silke","year":"2024","unstructured":"Silke, F. et al. Cost-effectiveness of interventions for HIV\/AIDS, malaria, syphilis, and tuberculosis in 128 countries: a meta-regression analysis. Lancet Glob. Health 12, e1159\u2013e1173 (2024).","journal-title":"Lancet Glob. Health"},{"key":"2259_CR55","unstructured":"Akers, J., Aguiar-Ib\u00e1\u00f1ez, R. & Baba-Akbari, A. Systematic reviews: CRD\u2019s guidance for undertaking reviews in health care (Centre for Reviews and Dissemination, 2009)."},{"key":"2259_CR56","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021).","journal-title":"BMJ"},{"key":"2259_CR57","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1586\/14737167.2015.1103185","volume":"16","author":"CC Adarkwah","year":"2016","unstructured":"Adarkwah, C. C., van Gils, P. F., Hiligsmann, M. & Evers, S. M. Risk of bias in model-based economic evaluations: the ECOBIAS checklist. Expert Rev. Pharmacoecon. Outcomes Res. 16, 513\u2013523 (2016).","journal-title":"Expert Rev. Pharmacoecon. Outcomes Res."},{"key":"2259_CR58","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.vhri.2014.06.008","volume":"4","author":"J Jakubiak-Lasocka","year":"2014","unstructured":"Jakubiak-Lasocka, J. & Jakubczyk, M. Cost-effectiveness versus cost-utility analyses: what are the motives behind using each and how do their results differ?-A Polish example. Value Health Reg. Issues 4, 66\u201374 (2014).","journal-title":"Value Health Reg. Issues"},{"key":"2259_CR59","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1111\/j.1523-1755.2004.00950.x","volume":"66","author":"PA McFarlane","year":"2004","unstructured":"McFarlane, P. A. & Bayoumi, A. M. Acceptance and rejection: cost-effectiveness and the working nephrologist. Kidney Int. 66, 1735\u20131741 (2004).","journal-title":"Kidney Int."},{"key":"2259_CR60","unstructured":"World Health Organization. The World Health Report 2002: Reducing Risks, Promoting Healthy Life (World Health Organization, 2002)."},{"key":"2259_CR61","unstructured":"Country Indexes and Weight. International Monetary Fund. Available at: https:\/\/data.imf.org\/regular.aspx?key=61015892."},{"key":"2259_CR62","unstructured":"Historical currency converter. Fxtop. Available at: https:\/\/fxtop.com\/en\/historical-currency-converter.php."},{"key":"2259_CR63","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-14-139","volume":"14","author":"C Crespo","year":"2014","unstructured":"Crespo, C., Monleon, A., Diaz, W. & Rios, M. Comparative efficiency research (COMER): meta-analysis of cost-effectiveness studies. BMC Med. Res. Methodol. 14, 139 (2014).","journal-title":"BMC Med. Res. Methodol."},{"key":"2259_CR64","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s40273-020-00914-6","volume":"38","author":"M Paulden","year":"2020","unstructured":"Paulden, M. Calculating and interpreting ICERs and net benefit. Pharmacoeconomics 38, 785\u2013807 (2020).","journal-title":"Pharmacoeconomics"},{"key":"2259_CR65","unstructured":"International Classification of Diseases 11th Revision. World Health Organization. Available at https:\/\/icd.who.int\/en."},{"key":"2259_CR66","unstructured":"World Bank Country and Lending Groups. The World Bank. Available at: https:\/\/datahelpdesk.worldbank.org\/knowledgebase\/articles\/906519-world-bank-country-and-lending-groups."},{"key":"2259_CR67","unstructured":"WHO country-region. World Health Organization. Available at: https:\/\/www.who.int\/countries."},{"key":"2259_CR68","doi-asserted-by":"publisher","first-page":"103375","DOI":"10.1016\/j.compbiomed.2019.103375","volume":"112","author":"B Remeseiro","year":"2019","unstructured":"Remeseiro, B. & Bolon-Canedo, V. A review of feature selection methods in medical applications. Comput. Biol. Med. 112, 103375 (2019).","journal-title":"Comput. Biol. Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02259-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02259-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02259-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:05:26Z","timestamp":1769508326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02259-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,24]]},"references-count":68,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2259"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02259-w","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,24]]},"assertion":[{"value":"14 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"W.C. receives research funding support from Illumina.Inc. and Optimum Patient Care Global. A.M. receives research funding support from Illumina.Inc. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"78"}}