The need for balancing 'black box' systems and explainable artificial intelligence: A necessary implementation in...

Area of Science:

• Medical Imaging and Diagnostics
• Artificial Intelligence in Healthcare
• Clinical Decision Support Systems

Background:

• Artificial Intelligence (AI), particularly machine and deep learning, is transforming radiology by processing large datasets and enhancing image analysis.
• Despite AI's potential, its integration into radiology faces significant hurdles, including data privacy, informed consent, and potential external interferences.
• Biases in AI algorithms, often due to unrepresentative data or insufficient training, can lead to skewed outcomes and worsen health disparities.

Purpose of the Study:

• To examine the impact of AI on radiology, highlighting its capabilities and challenges.
• To address ethical and legal considerations surrounding AI implementation in medical diagnostics.
• To advocate for the adoption of Explainable Artificial Intelligence (XAI) to improve transparency and patient understanding.

Main Methods:

• Review of current AI applications in radiology, focusing on machine learning and deep learning models.
• Analysis of ethical and legal implications, including data privacy, bias, and accountability for AI-induced errors.
• Discussion of the limitations of 'black box' AI models and the benefits of Explainable AI (XAI).

Main Results:

• AI excels at image analysis in radiology but presents risks such as data bias, 'hallucinations' from generative models, and opaque decision-making processes.
• Healthcare professionals remain liable for medical errors involving AI, as AI systems serve as supportive tools rather than replacements for human judgment.
• The 'black box' nature of many AI models hinders informed patient consent due to a lack of transparency in their reasoning.

Conclusions:

• Explainable Artificial Intelligence (XAI) is essential for enhancing transparency in AI-driven radiology.
• Prioritizing XAI allows patients to understand AI's role in their care, fostering trust and ethical practice.
• XAI promotes ethical standards by ensuring that AI's influence on clinical decisions is understandable and justifiable, even if performance is slightly reduced compared to black-box models.