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TRUVACE RECORD VERSION
record: TRV-2026-1134
version: 2
kind: revised
reason: Editorial pass: sharper headline; filed as context: no real AI gain or problem (gain activity_only, problem absent).
timestamp: 2026-11-04T09:55:42.909317Z
status: published
lens: general
sector: health
headline: How to apply artificial intelligence (AI) to facilitate and enhance MASH trials
dek: Introduction Metabolic dysfunction-associated steatohepatitis (MASH) is one of the most common liver diseases, but thus far only two drugs (resmetirom and semaglutide) have received conditional approval for use. Successful MASH trials are thus highly desired to fill the therapeutic and evidence gap. Concurrently, AI has powered advances in multiple areas of medicine. We consider in this review the potential applications of AI to enhance MASH trials. Methods We conduct a narrative review of AI uses in MASH, with…
gain_title: AI-based digital pathology models have been qualified by regulators for endpoint determination in MASH trials, with additional AI risk models and large language models applied to patient enrollment, recruitment and retention.
problem_title: (none)
trace_subject: (none)
gain_reading: AI-based digital pathology models have been qualified by regulators for endpoint determination in MASH trials, with additional AI risk models and large language models applied to patient enrollment, recruitment and retention.
gain_evidence: specific models have been qualified by the FDA and EMA for endpoint determination in MASH trials
problem_reading: (none)
problem_evidence: (none)
quick_read: Published September 16, 2026, this narrative review examines how AI is being applied to metabolic dysfunction-associated steatohepatitis trials, a field where only resmetirom and semaglutide had received conditional approval at the time of writing.

Regulatory qualification of AI pathology models for endpoint determination signals a shift in how liver disease trials are run, but the review remains largely descriptive of potential uses, leaving open how broadly these tools improve enrollment, retention, and trial validity in practice.
limitation: Evidence is presented as a narrative review of current and potential applications, not as new trial results measuring safety or efficacy outcomes.
tag: Context
key_points: MASH is described as one of the most common liver diseases with only two drugs having received conditional approval. | FDA and EMA have qualified specific AI models for endpoint determination in MASH trials. | Review also covers AI risk models for enrollment and large language models for patient interactions.
rundown: The review describes digital pathology and AI technologies for MASH grading as a mature application, noting regulatory qualification for trial endpoints.

It further details contemporary AI risk models to facilitate patient enrollment and large language models to support recruitment and retention, drawing lessons from other medical domains and current guidelines.
sources:
- peer_reviewed | Hepatology International | https://doi.org/10.1007/s12072-026-11150-z | 2026-09-16
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