AI Article Synopsis
- Trust and balanced reliance on digital resources are essential for accurate optical diagnoses and effective use of computer-aided diagnosis (CADx) in colonoscopy.
- Active learning with diverse polyp image datasets is beneficial for developing precise CADx systems, alongside enhancing doctors' digital literacy.
- Explainable AI improves interpretability of findings, helps communicate uncertainties to doctors, and fosters better collaboration by focusing on algorithm performance, transparency, and enhancing diagnostic skills.
Article Abstract
Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.dld.2023.11.033 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!