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To test this, we performed a large, fully reproducible benchmark of TabPFN, a leading FM for tabular prediction, against twelve established machine learning (ML) methods across twelve binary clinical tasks. Cohorts spanned 788\u2009\u2212\u2009139,528 patients across diverse outcomes, including survival, metastasis, and disease status. Using standardized preprocessing, bootstrapping, and multiple performance metrics, TabPFN was generally competitive but did not consistently outperform strong ML baselines. It exceeded the best ML model in only 16.7% of tasks, with most area under the receiver operating characteristic (AUROC) differences within \u00b1\u20090.02. TabPFN also incurred higher computational cost, with median runtimes 5.5\u00d7 longer and practical reliance on GPU acceleration. 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No new patient data were collected, and no identifiable information was handled at any point. Because all sources are open-access and de-identified, this work did not require institutional ethics approval or informed consent. All data analyses were performed in accordance with the data-use terms of the respective repositories and the principles of the Declaration of Helsinki.","order":1,"name":"Ethics","label":"Ethical consideration","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"J.N.K. declares ongoing consulting services for AstraZeneca and Bioptimus. Furthermore, he holds shares in StratifAI, Synagen, and Spira Labs, has received an institutional research grant from GSK and AstraZeneca, as well as honoraria from AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, Merck, MSD, BMS, Roche, Pfizer, and Fresenius. 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