Summary of Explainable Artificial Intelligence For Medical Applications: a Review, by Qiyang Sun et al.
Explainable Artificial Intelligence for Medical Applications: A Review
by Qiyang Sun, Alican Akman, Björn W. Schuller
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores how artificial intelligence (AI) is revolutionizing medical imaging diagnostics, disease prediction, and patient management. Researchers are developing robust machine learning algorithms that aid physicians in X-ray, CT scans, MRI diagnoses, and pattern recognition. AI also delivers prognoses on disease types and developmental trends for patients. Despite these advancements, there’s a pressing need to ensure the reliability of decision-making in medical scenarios. This paper reviews recent research on explainable artificial intelligence (XAI) with a focus on medical practices in visual, audio, and multimodal perspectives. The study aims to categorize and synthesize these practices, providing guidance for future researchers and healthcare professionals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is helping doctors diagnose diseases and predict patient outcomes using medical images like X-rays and MRIs. AI can also analyze sound waves from patients to make predictions about their health. This technology has been very helpful in the medical field, but it’s hard to know why AI is making certain decisions because it’s a “black box.” The paper looks at how researchers are working to make AI more explainable so that doctors and patients can trust its decisions. |
Keywords
* Artificial intelligence * Machine learning * Pattern recognition
