ReviewNature reviews. Gastroenterology & hepatology2025
Large language models for clinical decision support in gastroenterology and hepatology.
Review in Nature reviews. Gastroenterology & hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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
19 citing papers in PubMed.
- AI-Enabled Interpretation Guidance for Hemostasis Testing with TEGDiagnostics (Basel, Switzerland) · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Social Status and Clinical Resource Allocation by a Large Language Model: An Evaluation of 30,618 Decisions.Journal of personalized medicine · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Germline mutations and somatic mosaicism in steatotic liver diseases and related liver carcinogenesis.Nature reviews. Gastroenterology & hepatology · 2026Review
- Performance comparison of a neuro-symbolic large language model system versus conventional AI models and human experts in cholangitis management.BMC medical informatics and decision making · 2026Article
- Generative AI-Powered Virtual Assistant for Guideline-Directed Medical Therapy Optimization.JACC. Advances · 2026Article
- Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care.NPJ digital medicine · 2026Article
- Providing holistic care for patients with metabolic dysfunction-associated steatotic liver disease/metabolic dysfunction-associated steatohepatitis: Key aspects of clinical assessment and how to develop individualised care plans for surveillance and interventions.Diabetes, obesity & metabolism · 2026Review
- Artificial intelligence agents in cancer research and oncology.Nature reviews. Cancer · 2026Review
- Pain, uncertainty, and lack of clinical support drive emergency department utilization in cirrhosis: A qualitative study.Hepatology communications · 2026Article
- Is Artificial Intelligence Ready for Emergency Department Triage? A Retrospective Evaluation of Multiple Large Language Models in 39,375 Patients at a University Emergency Department.Journal of clinical medicine · 2026Article
- Multidisciplinary artificial intelligence systems versus single-model approaches for the diagnosis and management of ileus and volvulus.BMC gastroenterology · 2026Article
- Application of large language models as decision support tools in occupational health and safety management: a cohort study of industrial workers.Frontiers in public health · 2026Observational
- Deep learning and generative AI for medical imaging and clinical decision support systems: a structured critical review.Frontiers in digital health · 2026Review
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Responsible use of large language models in gastroenterology and hepatology.Therapeutic advances in gastroenterology · 2026Review
- ChatGPT improves usability, effectiveness, scalability, interpretability and accessibility, in early diagnosis of metabolic dysfunction-associated fatty liver disease.BMC gastroenterology · 2025Article
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical decision making in gastroenterology and hepatology has become increasingly complex and challenging for physicians. This growing complexity can be addressed by computational tools that support clinical decisions. Although numerous clinical decision support systems (CDSS) have emerged, they have faced difficulties with real-world performance and generalizability, resulting in limited clinical adoption. Generative artificial intelligence (AI), particularly large language models (LLMs), are introducing new possibilities for CDSS by offering more flexible and adaptable support that better reflects complex clinical scenarios. LLMs can process unstructured text, including patient data and medical guidelines, and integrate various information sources with high accuracy, especially when augmented with retrieval-augmented generation. Thus, LLMs can provide dynamic, context-specific support by generating personalized treatment recommendations, identifying potential complications based on patient history, and enabling natural language interactions with health-care providers. However, important challenges persist, particularly regarding biases, hallucinations, interoperability barriers, and proper training of health-care providers. We examine the parallel evolution of the complexity in clinical management in gastroenterology and hepatology, and the technical developments leading to current generative AI models. We discuss how these advances are converging to create effective CDSS, providing a conceptual basis for further development and clinical adoption of these systems.
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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.