Table 2.
Rank | Statements | Total score | Mean score |
---|---|---|---|
1 | AI for automatic detection, identification, characterization (type, size, severity), and differentiation of SB lesions: inflammatory lesions (erosion, ulceration, edema, etc.), vascular lesions, bulges, atrophy. | 174 | 9.15 |
2 | How do the overall results of AI and expert reading correlate (for all lesions/relevant lesions)? | 172 | 9.05 |
3 | Feasibility and accuracy in the real-world setting. | 171 | 9.00 |
4 | AI for automatic detection/identification, characterization (type, size, severity), and differentiation of colon lesions: ulcers, vascular lesions, polyps. | 157 | 8.26 |
5 | Auditing of CE systems after incorporation in clinical practice. | 155 | 8.15 |
6 | How to reduce the false-positive rate without decreasing sensitivity? | 155 | 8.15 |
7 | Creation of algorithms for simultaneous identification of multiple lesion types. | 151 | 7.94 |
8 | Adoption of AI by clinicians. | 150 | 7.89 |
9 | What are the optimal clinical end-points for the evaluation of AI software? | 149 | 7.84 |
10 | What are the optimal clinical trial design and end-points to compare different AI systems for CE? | 149 | 7.84 |
11 | How accurate should AI be to be incorporated in clinical trials? | 147 | 7.73 |
12 | What accuracy parameters are potential targets for AI utilization? | 145 | 7.63 |
AI, artificial intelligence; CE, capsule endoscopy; SB, small bowel.