The National Comprehensive Cancer Network (NCCN) hosted a policy summit in Washington, DC, on September 9 to explore the role of AI in cancer care both now and in the future.
Participants, including patients and patient advocates, clinicians, and policymakers, discussed AI’s emerging success in improving oncology care, as well as areas of possible concern.
Among topics raised were issues of implementation and integration into different platforms, oversight (both internal and governmental), and avoiding disparities and increasing access to AI-based software.
Collaboration between medical and technological organizations in the creation and implementation of AI tools for oncology was also highlighted, with the observation that many of the challenges faced with diagnostic AI software have also been faced in other fields that use AI applications.
The speed at which AI models are evolving was a common theme with panelists, NCCN said, with some comparing its potential to advances in care that represented major technological shifts, such as the transition to electronic medical records.
AI and cancer care was also the topic of a plenary session during the NCCN 2025 Annual Conference. Sessions are available for viewing at the NCCN Continuing Education Portal.
NCCN will be hosting a Patient Advocacy Summit on December 9 on the cancer care needs of veterans and first responders.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
