ReviewClinical and experimental medicine2026
Empowering liver cancer diagnosis and treatment with foundation models: technological innovation and clinical practice.
Review in Clinical and experimental medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.Frontiers in digital health · 2026Pooled it
- Foundation models for X-ray interpretation: a narrative review of current techniques and future perspectives in diagnostic imaging.Quantitative imaging in medicine and surgery · 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
9 authors.
Funding
Abstract
Foundation Models (FMs) are profoundly transforming the clinical management pathway for liver cancer. Their core value lies in enhancing diagnostic accuracy, enabling personalized therapeutic decision-making, and optimizing clinical efficiency through the integrative analysis of multimodal data, encompassing symptoms, medical histories, imaging findings, and genomic profiles. At the diagnostic level, FMs facilitate risk stratification by analyzing symptom and historical data while leveraging imaging features to aid in the early identification of lesions. Within the therapeutic decision-making domain for liver cancer, intelligent systems such as IDEAL provide critical clinical support by enabling precise hepatic anatomical reconstruction, quantitative assessment of surgical feasibility, and generation of individualized therapeutic recommendations for targeted agents and immunotherapy, thereby serving as essential adjuncts to HCC management. Despite their transformative potential, the clinical deployment of FMs faces significant core challenges: including concerns over model accuracy, reliability, and ethical issues such as data privacy and equitable access. Addressing these challenges requires interdisciplinary efforts focused on domain-specific model fine-tuning, robust ethical frameworks, and standardized regulatory guidelines. This review outlines the evolution and current state of FMs within healthcare, specifically highlighting their substantial application value in the liver cancer therapeutic landscape, and articulates the stage-specific challenges impeding their broader clinical adoption.
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.