Imaging AI is expanding from individual findings to broader interpretation, three-dimensional scans you can question, and restoration of degraded images. Yet radiology services still struggle to meet demand.

Jivesh Sharma, M.D., and Talia, an AI cohost, examine recent research including RADAR, NV-Reason-CT, nnFoundation and AMIFound. We then connect technical capability to a different question: can it help more people receive useful care?

The central lesson is that faster scan interpretation does not automatically mean care arrives sooner. The episode distinguishes model benchmarks, reader studies, measured screening workload and completed follow-up, with visual explanations of where the next bottleneck can appear.

Thank you for watching or listening. Follow HealthIT.com for future conversations; on YouTube, like this episode and subscribe. What imaging task or access problem would you like us to examine next? Send suggestions at https://www.healthit.com/feedback.

This episode uses an authorized AI-generated version of Jivesh Sharma's voice and an AI cohost. General education, not personal medical advice. Conceptual illustrations and hypothetical pathway animations are labelled; they are not patient scans or clinical model outputs.

Chapters
00:00 The prediction and the paradox
00:46 RADAR: one scan, many questions
01:42 NV-Reason-CT: questioning a 3D scan
02:39 nnFoundation and the pace of progress
03:29 AMIFound: a clearer image can still be wrong
04:19 Why radiologists remain in demand
05:19 MASAI and the bottleneck in care
06:54 EAGLE: detection needs completed follow-up
07:59 What to watch next
08:56 Thank you and invitation

Primary references
- RADAR, Science: https://www.science.org/doi/10.1126/science.aec6129
- NV-Reason-CT: https://huggingface.co/nvidia/NV-Reason-CT
- NV-Reason-CT preprint: https://arxiv.org/abs/2609.27511
- nnFoundation: https://arxiv.org/abs/2609.26924
- AMIFound: https://www.nature.com/articles/s42256-026-01310-z
- MASAI: https://pubmed.ncbi.nlm.nih.gov/39904652/
- MASAI interval cancers: https://pubmed.ncbi.nlm.nih.gov/41620232/
- EAGLE: https://www.nature.com/articles/s41591-026-04656-4
- RCR workforce census: https://www.rcr.ac.uk/media/n1fjvrv4/rcr-2025-clinical-radiology-workforce-census-report.pdf
- ACR workforce economics: https://www.acr.org/Clinical-Resources/Publications-and-Research/ACR-Bulletin/2026/workforce-economics
- Hinton original recording: https://www.youtube.com/watch?v=2HMPRXstSvQ