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Related Experiment Video

Updated: Jul 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated

Fatemeh Ahouz¹, Mohammad Bagher Sohrabi², Amin Golabpour³

¹Department of Computer Engineering, Faculty of Energy and Data Science, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.

BMC Medical Informatics and Decision Making

|July 7, 2026

Summary

This study introduces a human-centered AI model that generates clinically validated diagnostic rules, bridging the gap between AI and healthcare professionals for improved trust and utility in medical AI.

Keywords:

AI-based clinical decision making Diagnostic techniques and procedures Human-centered artificial intelligence Medical informatics Medical knowledge engineering Meta-heuristic algorithms NSGA II Rule extraction algorithms

Related Experiment Videos

Last Updated: Jul 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence in Healthcare
Medical Informatics
Clinical Decision Support Systems

Background:

Artificial intelligence (AI) is transforming healthcare delivery, but a gap exists between AI models and clinical experts.
Human-Centered AI offers a solution to bridge this communication gap.

Purpose of the Study:

To introduce a human-centered rule extraction model for AI diagnostic tools.
To enhance the interpretability and clinical utility of AI in healthcare.

Main Methods:

Utilized the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for rule extraction.
Developed a model that autonomously generates diagnostic rules and adjusts variable thresholds.
Incorporated clinical expert evaluation for rule validation and real-world applicability.

Main Results:

The model surpassed existing methods in predictive value accuracy (PVA) and support on benchmark datasets (WBC, WDBC, Pima).
Achieved interpretability comparable to black-box methods while maintaining high accuracy.
Clinically validated rules by 13 physicians, with a Content Validity Index (CVI) of at least 0.85.
Provided multiple high-performance diagnostic rules per class for clinical flexibility.

Conclusions:

Emphasizes multidisciplinary collaboration between AI specialists and healthcare professionals.
Aims to build trust in AI diagnostic systems through transparency and clinical validation.