Nucs AI, a company developing advanced imaging-based AI to support precision theranostics, has formed its medical advisory board, which includes clinical leaders in radiology, nuclear medicine, oncology, and clinical research.
The company formed the board to ensure that Nucs AI's technology development and clinical strategy remain aligned with clinical practice as the company advances toward broader validation and adoption. The company is focused on supporting more informed, personalized treatment decisions by helping clinicians better understand how disease responds to therapy over time.
The board includes the following members:
- Jeremie Calais, MD, PhD, medical advisor: director, University of California, Los Angeles (UCLA) nuclear medicine and theranostics clinical research program; associate professor, Ahmanson Translational Theranostics Division, department of nuclear medicine and theranostics
- A. Omer Nawaz, PhD, independent scientific advisor: head of theranostics and radiation Science, oncology R&D, AstraZeneca
- Francesco Ceci, MD, PhD, medical advisor: director, nuclear medicine and theranostics, IEO European Institute of Oncology, Milan, Italy; associate professor, department of oncology, University of Milan
- Murray Becker, MD, PhD, medical advisor: University Radiology Group; associate clinical professor, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ
![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)
