Explainable AI for Healthcare

Area of Science:

• Computer Science
• Artificial Intelligence
• Healthcare Technology

Background:

• Artificial intelligence (AI) integration in healthcare is rapidly advancing, impacting diagnosis, treatment, and patient care.
• Complex AI models often lack transparency, posing risks in high-stakes medical decision-making.
• Explainability is vital for informed clinical decisions and patient trust in AI.

Purpose of the Study:

• To introduce foundational concepts of explainable AI (XAI).
• To analyze XAI methodologies for enhancing AI model interpretability in healthcare.
• To discuss XAI applications across diverse healthcare data modalities.

Main Methods:

• Review of foundational explainable AI (XAI) concepts.
• Analysis of prominent methodologies for enhancing AI model interpretability.
• Examination of XAI applications across time-series, text, image, and audio medical data.

Main Results:

• XAI provides transparency essential for healthcare professionals and patients.
• XAI addresses unique challenges across various medical data types.
• Identified prominent XAI methodologies applicable to healthcare domains.

Conclusions:

• XAI is critical for safe and effective AI implementation in healthcare.
• Current limitations and challenges in healthcare XAI implementation were discussed.
• Promising future research directions for enhancing AI reliability in healthcare were identified.