Transparency of medical artificial intelligence systems

Area of Science:

• Medical Artificial Intelligence
• Healthcare Technology
• Clinical Decision Support Systems

Background:

• Artificial intelligence (AI) systems are increasingly integrated into healthcare, offering potential for improved diagnostics and treatment recommendations.
• The
• black box
• nature of many medical AI systems hinders trust and verification among patients, providers, and regulators.
• Transparency is essential for understanding AI design, operation, and outcomes in clinical settings.

Purpose of the Study:

• To examine the current state of transparency in medical AI systems.
• To identify challenges and risks associated with opaque AI systems in healthcare.
• To explore techniques promoting explainability and discuss regulatory frameworks for trustworthy AI.

Main Methods:

• Review of current literature on transparency and explainability in medical AI.
• Analysis of challenges in the AI machine learning pipeline, from data to deployment.
• Exploration of techniques for promoting explainability and continual monitoring.

Main Results:

• Many medical AI systems currently function as "black boxes," posing challenges for interpretation and trust.
• Various techniques can enhance explainability across the AI pipeline, from training data to model deployment.
• Overcoming barriers to integrating transparency tools and establishing clear regulatory frameworks are key.

Conclusions:

• Ensuring transparency in medical AI is vital for building trust and enabling ethical clinical integration.
• Explainable AI techniques and robust regulatory oversight are necessary for reliable and responsible AI healthcare solutions.
• Continued research and collaboration are needed to address challenges and advance trustworthy AI in medicine.