Trending Topics in Gastroenterology | GastroAGI

AI and U.S. Healthcare Costs: NEJM Catalyst | July 2026

Introduction: Artificial intelligence is rapidly transforming healthcare through drug discovery, clinical decision support, remote monitoring, and administrative automation. While AI is widely expected to reduce healthcare costs, this perspective argues that current payment models and market dynamics may instead increase healthcare spending despite improving quality and access. Key Takeaways: • AI is likely to improve healthcare quality, efficiency, and patient access, but these benefits will not automatically translate into lower healthcare costs. • Under the current fee-for-service (FFS) payment model, AI may actually increase healthcare utilization and total spending by expanding diagnostic testing, patient monitoring, and clinical services. • AI has the potential to accelerate drug discovery and personalized medicine, but these innovations may initially increase pharmaceutical expenditures. • Administrative automation may improve operational efficiency, yet cost savings may not be passed on to patients because of existing healthcare market structures. • Value-based care provides a stronger financial framework for AI to reduce unnecessary care, improve outcomes, and slow long-term spending growth. • Highly consolidated hospital systems and insurance markets may limit the ability of AI-driven efficiencies to reduce healthcare expenditures. • Policymakers and payers will need to redesign payment models, reimbursement policies, and regulatory frameworks if AI is expected to generate meaningful cost savings. • Without aligning financial incentives, AI is more likely to improve healthcare than reduce its cost. Clinical Impact: AI should be viewed primarily as a tool to enhance clinical outcomes, expand access, and improve efficiency rather than an immediate solution for reducing healthcare expenditure. The economic impact of AI will depend more on healthcare policy and reimbursement reform than on technological capability alone. Bottom Line: Artificial intelligence alone will not solve the healthcare cost crisis. Its ability to reduce spending depends on transitioning from fee-for-service to value-based care, supported by policies that align AI innovation with better outcomes rather than greater healthcare utilization.

Large AI Models and Healthcare: Nature Medicine | June 2026

Introduction: Large frontier AI models such as GPT-5 and Gemini have achieved impressive results across numerous healthcare benchmarks. However, high benchmark scores alone may not reflect real-world clinical reliability. This study systematically evaluated the robustness of leading AI models using adversarial testing and clinician-guided assessment. Why was this study needed? • AI models are increasingly being proposed for clinical decision support. • Benchmark performance may overestimate real-world clinical capability. • Robustness and reliability are essential before deployment in healthcare. • Multimodal medical reasoning remains insufficiently validated. • Better evaluation frameworks are needed to ensure safe AI implementation. Results: • Leading frontier AI models demonstrated significant vulnerability to simple adversarial changes, often producing incorrect answers despite previously excellent benchmark performance. • AI systems could sometimes guess correct answers even when critical clinical information was removed, while becoming confused by minor prompt modifications and generating convincing—but incorrect—reasoning. • Current healthcare benchmarks vary considerably in what they actually measure, highlighting a substantial gap between benchmark success and true clinical readiness. Clinical Impact: This study serves as an important reminder that high benchmark accuracy does not guarantee safe clinical performance. Before widespread adoption, healthcare AI should undergo rigorous real-world validation, stress testing, and clinician-led evaluation to ensure robustness, transparency, and patient safety. Bottom Line: AI is highly capable—but not yet fully reliable for autonomous clinical decision-making. Robustness, consistency, and real-world validation should become the next benchmarks before frontier AI models are integrated into routine healthcare.

AI Ethics From Silicon Valley to the Vatican: JAMA | July 2026

Introduction: Artificial intelligence is rapidly transforming medicine, but its influence extends far beyond healthcare. This JAMA AI Conversations article explores how AI ethics has become a global societal issue, engaging technology leaders, policymakers, healthcare professionals, and even the Vatican. Why was this article needed? As AI becomes increasingly embedded in clinical practice and everyday life, concerns about transparency, safety, fairness, accountability, and human values have moved beyond the technology sector. Broader ethical guidance is needed to ensure AI serves humanity responsibly. What did the article show? • AI ethics is evolving from a technical discussion into a global societal dialogue. • The Vatican's engagement highlights the growing recognition that AI development is fundamentally a human and ethical challenge. • Interpretability, transparency, governance, and safety remain central to trustworthy AI. • Healthcare is emerging as one of the most important sectors for responsible AI implementation. • Collaboration between technology companies, clinicians, ethicists, governments, and international organizations is essential for developing safe AI systems. • Future AI regulation should balance innovation with patient safety, human dignity, equity, and public trust. Clinical Impact: As AI becomes integrated into clinical decision-making, healthcare professionals will need to understand not only how AI performs but also how it is developed, governed, and ethically deployed. Responsible AI implementation will require transparency, human oversight, and patient-centered values. Take-Home Message: The future of medical AI will be determined not only by technological advances but also by the ethical principles that guide its development. Building trustworthy AI requires collaboration among clinicians, scientists, policymakers, industry, and society to ensure innovation remains aligned with human values.

Physician-Complementing AI in Oncology: The ASCO Post | June 2026

Introduction: Artificial intelligence is rapidly transforming oncology, evolving from image interpretation and pathology analysis to supporting complex clinical decision-making. This perspective argues that AI should enhance the capabilities of oncologists rather than replace their expertise. Why was this article needed? As AI becomes increasingly integrated into cancer care, there is growing debate over whether it should substitute physicians or function as a tool that augments clinical judgment. The authors advocate for a physician-centered model of AI adoption. What did the article show? • The authors distinguish physician-substituting AI from physician-complementing AI, favoring the latter approach. • AI can reduce administrative burden through ambient documentation and automated electronic medical record workflows. • AI can synthesize vast clinical, genomic, laboratory, and real-world data beyond human cognitive capacity. • Decision-support AI may help oncologists personalize treatment by integrating multiple patient-specific variables. • AI can incorporate patient preferences, quality-of-life goals, and comorbidities into therapeutic decision-making. • Rather than replacing oncologists, AI enables clinicians to spend more time on patient communication, empathy, and shared decision-making. • The future of precision oncology will depend on close collaboration between physician expertise and AI-driven intelligence. Clinical Impact: Physician-complementing AI has the potential to improve clinical efficiency, personalize treatment decisions, and support evidence-based oncology while preserving the essential role of physician judgment and the doctor–patient relationship. Take-Home Message: The greatest value of AI in oncology lies not in replacing physicians but in amplifying their expertise. By combining human clinical judgment with AI-powered decision support, cancer care can become more precise, efficient, and patient-centered.

AI-Based Clinical Trial End Points: A New Era in Drug Development: NEJM AI | July 2026

Introduction Clinical trial endpoints have traditionally relied on expert human interpretation, particularly for pathology-based outcomes. However, variability between observers, cost, and time remain important limitations. This NEJM AI perspective discusses how artificial intelligence is beginning to transform endpoint assessment in clinical trials. Why Was AI Needed? * Histological assessment is often subjective. * Significant interobserver variability exists among expert pathologists. * Manual review is labour-intensive and time-consuming. * Inconsistent interpretation may affect trial outcomes and regulatory decisions. * Standardised and reproducible assessment has long been a major unmet need. What Has Changed? * In 2025, the FDA and EMA endorsed **AIM-NASH**, the first AI-enabled clinical trial end point. * AIM-NASH uses deep learning to evaluate liver biopsy histology in MASH clinical trials. * AI demonstrated high agreement with expert pathologists while improving consistency. * This represents the first regulatory acceptance of an AI-based efficacy end point. * The approval establishes a regulatory pathway for future AI-enabled biomarkers. Potential Clinical Impact * More objective assessment of treatment response. * Reduced observer variability. * Improved patient selection. * Faster central pathology review. * Shorter and potentially less expensive clinical trials. * Better standardisation across international multicenter studies. * Potential expansion to oncology, gastroenterology, cardiology, radiology, and other specialities. Take-Home Messages * AI has moved from decision support to regulatory-accepted clinical trial evaluation. * AIM-NASH represents a historic milestone in AI-assisted drug development. * AI is expected to complement—not replace—clinical experts and pathologists. * Rigorous validation and regulatory oversight remain essential. * This approval may fundamentally reshape the design and conduct of future clinical trials.

Medical AI Assistant: Publication or Medical Device?: NEJM AI | July 2026

Introduction: As artificial intelligence becomes increasingly integrated into clinical practice, an important question arises: should AI assistants be regulated as medical devices or viewed as evidence-based clinical methodologies? This NEJM AI perspective proposes a new framework that could influence the future regulation and adoption of medical AI. Why was this article needed?: Current regulatory pathways primarily treat clinical AI systems as medical devices. However, this approach may not be appropriate for open-source AI assistants that are transparent, peer-reviewed, and used by physicians as decision-support tools. The author argues that an alternative regulatory model is needed. What did the article show?: The author introduces the concept of a Medical Artificial Intelligence Assistant (MAIA)—an open-weight generative AI model integrated with a patient's health records through a secure vector database and used by physicians via an open-source interface. If the AI architecture, retrieval methods, and clinical validation are published in peer-reviewed journals, MAIA should be regarded as a published clinical methodology rather than a commercial medical device. Physicians using such validated systems are applying evidence from the medical literature, similar to adopting a published clinical guideline or risk score. Clinical Impact: This framework could encourage greater transparency, peer review, independent validation, and open scientific collaboration while reducing dependence on proprietary "black-box" AI systems. It also highlights the importance of physician oversight, reproducibility, and evidence-based implementation as AI becomes part of routine clinical practice. Take-Home Message: This perspective challenges the traditional view that every clinical AI tool should be regulated as a medical device. Instead, it proposes that transparent, peer-reviewed, open-source AI assistants may evolve into evidence-based clinical methodologies, potentially reshaping AI regulation, physician adoption, and the future standard of care.

LLMs Rapidly Transform European Gastroenterology Practice : Gut | June 2026

Introduction: Large language models (LLMs), exemplified by ChatGPT and similar artificial intelligence platforms, are rapidly reshaping healthcare delivery, medical education, and scientific research. In gastroenterology, these technologies have the potential to support clinical decision-making, streamline documentation, enhance endoscopy workflows, and improve academic productivity. Despite their growing visibility, real-world data on how gastroenterologists are using LLMs in daily practice have been limited. Problem Statement: The widespread adoption of LLMs has occurred faster than the development of formal training programs, governance frameworks, and specialty-specific guidance. Understanding current patterns of use, perceived benefits, concerns, and barriers is essential for ensuring safe and effective integration of these technologies into gastroenterology practice. Summary: The EuroGI-AI project provides the largest assessment to date of LLM adoption among European gastroenterologists. The survey demonstrated that more than half of respondents already use LLMs in their clinical or academic activities, with many engaging with these tools on a weekly or daily basis. Educational purposes, clinical decision support, and scientific writing emerged as the most common applications. Among academic users, literature summarization and language refinement were particularly valued, highlighting the growing role of AI in research productivity. Most respondents considered LLMs reliable and believed they improve professional efficiency and clinical outcomes. Notably, many clinicians also recognized potential applications within endoscopy practice. However, concerns regarding inaccuracies remained common and were associated with reduced trust in AI-generated outputs. Despite widespread use, formal training was uncommon, yet there was overwhelming support for structured educational programs and incorporation of AI competencies into gastroenterology curricula. Institutional restrictions, financial costs, technological complexity, and time limitations were identified as key barriers to broader adoption. Overall, the findings suggest that LLMs are rapidly becoming integrated into European gastroenterology practice. Future success will depend on specialty-specific development, structured training, robust governance frameworks, and ongoing evaluation to ensure safe, ethical, and clinically meaningful implementation of AI technologies.

Competing Risk Analysis in HCT & IEC Research : Transplant Cell Ther | May 2026

Introduction Competing risks are frequently encountered in hematopoietic cell transplantation (HCT) and immune effector cell (IEC) therapy research, particularly when mutually exclusive outcomes coexist. A classical example is treatment-related mortality (TRM) competing with disease relapse, where early non-relapse death precludes the possibility of relapse occurrence. Mismanagement of competing risks can produce biased survival estimates and misleading hazard interpretations, making proper statistical handling essential in transplantation and cellular therapy studies. Problem Statement Competing risks are commonly analyzed incorrectly using standard Kaplan–Meier (KM) methods that censor competing events, violating the assumption of non-informative censoring. This leads to systematic overestimation of event probabilities. In addition, Fine–Gray subdistribution hazard models are frequently misinterpreted as causal models despite their prognostic—not etiologic—nature. Many clinicians remain unfamiliar with the conceptual distinctions between cumulative incidence, cause-specific hazards and subdistribution hazards, limiting the quality and interpretability of transplant outcome research. Summary This practical review provides a clinician-oriented primer on competing risk methodology with a detailed stepwise guide for implementation using the open-access R and RStudio statistical environment. The authors first explain the limitations of naïve Kaplan–Meier analysis in competing-risk settings. When competing events are censored using standard KM methods, the resulting 1-KM estimator inflates the true probability of the event of interest because it ignores the dependency between mutually exclusive outcomes. The review emphasizes the importance of the cumulative incidence function (CIF), which appropriately accounts for the probability of remaining free from competing events. Unlike KM estimation, CIF conditions the risk of the primary event on the absence of competing outcomes over time, generating more accurate cumulative risk estimates. Gray’s test is highlighted as the preferred statistical method for comparing CIF curves between groups. The manuscript also clarifies the distinction between cause-specific hazard ratios (HRcs) and subdistribution hazard ratios (HRsd). Cause-specific Cox proportional hazard models censor competing events and estimate instantaneous event rates among individuals still at risk, making them more suitable for mechanistic or etiologic inference. In contrast, Fine–Gray models retain competing events in the risk set and directly model cumulative incidence, making HRsd more useful for patient-level prognostic prediction rather than causal interpretation. A key conceptual message is that Fine–Gray models may generate misleading associations when variables strongly influence competing events. For example, a covariate associated with higher TRM may appear to reduce relapse incidence simply because patients die before relapse can occur. The authors demonstrate through simulations that HRsd estimates become increasingly biased as the association with competing events strengthens, reinforcing the need for cautious interpretation of Fine–Gray regression outputs. The second half of the review provides a practical tutorial for conducting competing risk analyses in R. Using a real-world allogeneic HCT dataset in acute myeloid leukemia, the authors illustrate cumulative incidence estimation, visualization, Gray’s testing, cause-specific Cox regression and Fine–Gray regression modeling. Multiple R packages are introduced, including tidycmprsk, survival, ggsurvfit, and gtsummary, enabling generation of publication-ready cumulative incidence plots and regression tables. The review concludes by emphasizing that Fine–Gray models are best suited for prognostic estimation, whereas cause-specific Cox models are preferable for etiologic or mechanistic research questions. The authors strongly advocate collaboration with trained biostatisticians to minimize methodological bias and improve rigor in transplantation and cellular therapy research involving competing risks.

AI Meets Endoscopy for early ESCC Detection: Endoscopy | April 2026

Introduction Esophageal squamous cell carcinoma is a highly aggressive malignancy where early detection and accurate assessment of invasion depth are critical for determining optimal treatment strategies. Current tools such as magnifying endoscopy and endoscopic ultrasonography (EUS) are effective but operator-dependent. Advances in deep learning offer an opportunity to enhance diagnostic accuracy through automated image analysis. Problem Statement Existing diagnostic methods for early ESCC are limited by variability in expertise and lack a reliable, integrated system for simultaneous detection and invasion depth assessment. Summary This study introduces MUMA-EDx, a novel multimodal deep learning model that integrates magnifying endoscopy and EUS imaging to improve both detection and staging of early ESCC. The model was trained on a large retrospective dataset and validated prospectively, demonstrating excellent performance. For tumor detection, the model achieved outstanding accuracy with an AUC of 0.94 in retrospective validation and 1.00 in prospective testing. For invasion depth classification—a more complex and clinically critical task—the model maintained strong performance, outperforming novice endoscopists and approaching expert-level accuracy. The key innovation lies in combining multiple imaging modalities, which significantly improved diagnostic precision compared to single-modality approaches. Clinically, this tool has the potential to standardize ESCC diagnosis, reduce operator dependency, and guide more accurate treatment decisions. Key takeaway: Multimodal AI can match expert performance and may redefine early cancer detection and staging in endoscopy.

AI-Based Risk Prediction for HCC: Cancer Discovery | April 2026

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally, largely due to late diagnosis and inadequate risk stratification. Current clinical risk scores have limited predictive accuracy and often fail to identify high-risk individuals early. With the increasing availability of large-scale healthcare data, machine learning offers an opportunity to improve early detection using routinely collected clinical information. Problem Statement Existing HCC risk prediction models are insufficient in accuracy, lack generalizability across populations, and often rely on limited variables. There is a need for a scalable, interpretable, and robust model that can integrate diverse real-world clinical data to accurately stratify HCC risk and enable early detection at a population level. Summary This large multicentric study developed an interpretable machine-learning model (PRE-Screen-HCC) using data from over 900,000 individuals across UK Biobank and the All of Us cohort. The model integrated demographics, lifestyle factors, clinical records, laboratory data, genomics, and metabolomics. It significantly outperformed existing risk scores in predicting HCC risk across diverse populations. Importantly, the model is transparent, externally validated, and made accessible via a web-based calculator, making it clinically applicable. This study represents a major step toward precision screening and early detection of HCC using real-world data and AI.