Related Experiment Video
Updated: Feb 28, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Clinical Literature Analysis and Review using Interpretive Transparency (CLARITY): An Exploratory Scaffold for
Andrew M Ball1, Tommy Perreault2, Jan Dommerholt3
1Spine and Headache Rehabilitation Lake Norman, Novant Rehabilitation, Novant Health, Charlotte, NC.
Objective:
To describe and evaluate Clinical Literature Analysis and Review using Interpretive Transparency (CLARITY), an artificial intelligence (AI)-assisted literature synthesis scaffold designed for structured learning and hypothesis generation in rehabilitation medicine.
Design:
Proof-of-concept case series using ScholarAI, which integrates GPT-assisted triage and fairness-aware reweighting. All outputs are exploratory and not for clinical application.
Setting:
Open-access rehabilitation literature applied in academic and clinical training environments.
Main Outcome Measures:
Synthesis speed, adjudicator concordance, demographic representation, and fairness-adjusted effect sizes.
Results:
CLARITY reduced review timelines from months to days, achieving 84.7% concordance in clinician adjudication of borderline studies. Fairness adjustments (±20%) modestly altered pooled estimates and highlighted underrepresentation trends. SHAP and LIME overlays improved transparency but did not affect inclusion outcomes.
Conclusions:
CLARITY is an AI-assisted educational scaffold (not yet a clinical decision aid tool), supporting efficient, reproducible, equity-conscious engagement with literature to stimulate hypothesis generation and critical dialogue. Although limited by single-reviewer design and absence of audit logs, it provides a valuable entry point for structured AI-assisted synthesis in early-stage or educational contexts. Outputs are strictly exploratory and require expert interpretation.
