ReviewJournal of translational medicine2026
Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.
Review in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs artificial intelligence (AI) has evolved through a series of discrete leaps, the Foundation model (FM) has demonstrated substantial potential for applications in the medical domain. Built on scalability, multimodal processing, and adaptability to diverse downstream tasks, FMs offer a flexible framework that can be tailored to various clinical needs. Nevertheless, the translation of FMs into clinical practice remains challenged by concerns regarding data privacy and security, bias and fairness, interpretability and sustainability. Therefore, a clinically oriented review is needed not only to summarize current advances and limitations but also to emphasize the clinical relevance, practical significance, and translational implications of FMs in medicine. MAIN BODY: This review outlines the development history of AI and introduces the FM basic theory, summarizes recent advances in their medical applications, and examines how FMs may support clinicians, enhance workflow efficiency, and improve patient outcomes. In addition to summarizing existing work, this review places particular emphasis on the clinical relevance, practical significance, and translational challenges of FMs across healthcare. Furthermore, privacy, safety, transparency, computational resources, clinical feasibility and sustainability issues are further discussed. Finally, the future direction of FMs in the medical field was projected.
conclusionA central concept of this review is that the clinical translation of FMs requires interdisciplinary collaboration among AI developers, clinicians, and policymakers, supported by careful evaluation frameworks and continuous oversight to ensure clinical benefit and minimize risk.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.