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Related Experiment Videos

An Explainable AI Framework for Continuous Monitoring, Risk Stratification, and Clinical Decision Support in Primary

Basile Njei1, Ulrick Sidney Kanmounye2

  • 1Engelhardt School of Global Health and Bioethics, Euclid University, Avenue de France, Campus ENAM, Bangui, BP 157, Central African Republic, +236 21 61 59 2.

JMIR Research Protocols
|June 25, 2026
PubMed
Summary

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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

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This summary is machine-generated.

This study introduces AIm-PBC, an explainable AI framework for continuous monitoring and risk prediction in primary biliary cholangitis (PBC). It aims to improve clinical decision support and patient outcomes by integrating diverse data for better disease management.

Area of Science:

  • Hepatology
  • Artificial Intelligence
  • Clinical Decision Support

Background:

  • Current primary biliary cholangitis (PBC) management relies on limited static markers and lacks integrated assessment of symptoms and noninvasive risk stratification for clinically significant portal hypertension (CSPH).
  • Existing tools fail to combine longitudinal data, elastography, and patient-reported outcomes, leading to inconsistent clinical actions for risk assessment.

Purpose of the Study:

  • To develop, validate, and pilot AIm-PBC, an explainable AI framework for continuous disease monitoring in PBC.
  • To enable early prediction of CSPH complications and provide guideline-based clinical decision support for PBC patients.

Main Methods:

  • A multiphase study involving over 600 adults with PBC, integrating retrospective and prospective data.
Keywords:
AIartificial intelligenceclinical decision supportexplainable artificial intelligencepatient-reported outcomesportal hypertensionprimary biliary cholangitisrandomized controlled trials

Related Experiment Videos

  • Utilizing an AI framework with longitudinal biochemical markers, elastography, and patient-reported outcomes to predict CSPH complications.
  • Deploying the AI framework via a SMART-on-FHIR-enabled electronic health record clinical decision support tool for evaluation.

    • Main Results:

    • Primary outcomes include the calibration and responsiveness of a novel disease activity index and the discriminatory performance of the CSPH prediction model.
    • The study will assess the effectiveness of the decision support tool through improvements in guideline-concordant care, usability, and clinician workload.
    • Secondary outcomes focus on fairness metrics and workflow efficiency of the AI framework.

    Conclusions:

    • The AIm-PBC framework offers a scalable and explainable approach to bridge gaps in PBC monitoring, risk prediction, and clinical action.
    • Successful implementation of AIm-PBC has the potential to enhance early complication detection, improve symptom management, and support equitable, evidence-based care in routine hepatology practice.