AI2030 Responsible AI Tools Marketplace

Key Takeaways

AI is transforming medical diagnosis across multiple domains — from analyzing X-rays, CT scans, and MRIs to recognizing disease patterns in acoustic data and predicting disease progression, but these powerful tools create a critical trust problem that must be solved before widespread clinical adoption.
The "black box" problem is a major barrier to responsible AI in healthcare — while AI algorithms can make accurate predictions, they often cannot explain why they reached a particular diagnosis or recommendation, which conflicts with the medical field's fundamental requirement for accountability and transparency in patient care.
Explainable AI (XAI) is essential for clinical decision-making — the paper emphasizes that healthcare professionals need to understand and verify the reasoning behind AI recommendations before trusting them with patient care, making interpretability not just a technical feature but a medical necessity.
Multiple approaches to explainability exist across different data types — the review categorizes XAI methods across visual (imaging), audio, and multimodal medical applications, suggesting that different types of medical data may require tailored explanation strategies rather than one-size-fits-all solutions.
Bridging the AI-medicine gap requires collaboration between sectors — the paper identifies that solving the reliability and transparency challenge demands ongoing partnership between AI researchers and healthcare professionals to ensure AI systems meet the unique accountability standards of medical practice.

Abstract

The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.

Key Takeaways

AI is transforming medical diagnosis across multiple domains — from analyzing X-rays, CT scans, and MRIs to recognizing disease patterns in acoustic data and predicting disease progression, but these powerful tools create a critical trust problem that must be solved before widespread clinical adoption.
The "black box" problem is a major barrier to responsible AI in healthcare — while AI algorithms can make accurate predictions, they often cannot explain why they reached a particular diagnosis or recommendation, which conflicts with the medical field's fundamental requirement for accountability and transparency in patient care.
Explainable AI (XAI) is essential for clinical decision-making — the paper emphasizes that healthcare professionals need to understand and verify the reasoning behind AI recommendations before trusting them with patient care, making interpretability not just a technical feature but a medical necessity.
Multiple approaches to explainability exist across different data types — the review categorizes XAI methods across visual (imaging), audio, and multimodal medical applications, suggesting that different types of medical data may require tailored explanation strategies rather than one-size-fits-all solutions.
Bridging the AI-medicine gap requires collaboration between sectors — the paper identifies that solving the reliability and transparency challenge demands ongoing partnership between AI researchers and healthcare professionals to ensure AI systems meet the unique accountability standards of medical practice.

Abstract

Explainable Artificial Intelligence for Medical Applications: A Review

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Explainable Artificial Intelligence for Medical Applications: A Review

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links