Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare.

Area of Science:

• Artificial Intelligence in Healthcare
• Medical Data Analysis
• Explainable AI (XAI)

Background:

• Lack of interpretability in AI hinders adoption in healthcare, leading to accountability issues and reduced prediction quality.
• Clinician trust in complex AI models is crucial for effective healthcare integration.
• Global data protection regulations emphasize the need for plausible and verifiable AI predictions.

Purpose of the Study:

• To design an interpretable AI model that enhances transparency and trustworthiness in medical predictions.
• To develop algorithms that mimic human-like reasoning for AI-driven healthcare decisions.
• To provide high-fidelity explanations for AI model predictions using statistical analysis.

Main Methods:

• Developed an interpretability-based AI model utilizing statistical analysis of medical datasets.
• Calculated relative weights of features from medical images and patient symptoms to determine variable importance.
• Used relative weights to derive positive and negative probabilities of disease, offering clear explanations.

Main Results:

• The model demonstrated accuracy and provided insights into its prediction processes.
• Relative weights effectively represented variable importance in predictive decision-making.
• Experiments on COVID-19 datasets confirmed the model's effectiveness and interpretability.

Conclusions:

• The developed interpretable AI model enhances trust and accountability in healthcare predictions.
• The model's human-like reasoning and clear explanations facilitate clinician understanding and adoption.
• The approach offers a promising solution for explainable AI in medical diagnostics.