Around 90% of radiology training in Valencia, Spain, is now being done face-to-face rather than online, according to Dr. Luis Martí-Bonmatí, PhD, the winner of the 2021 EuroMinnies award for Most Effective Radiology Educator. Martí-Bonmatí discusses his team's approach to training, and also shares his thoughts on books, music, and wine.
Martí-Bonmatí is director of the Medical Imaging Clinical Unit and chairman of the radiology department and of the Biomedical Imaging Research Group at La Fe University and Polytechnic Hospital and La Fe Health Research Institute, Valencia. He also co-founded Quantitative Imaging Biomarkers in Medicine (QUIBIM), which is dedicated to artificial imaging and medical image processing, analysis, extraction, and measurement of radiomic data and imaging biomarkers used in clinical diagnostics, research projects, and clinical trials, and he is vice president of the Federation of European Academies of Medicine.
In this video interview, he talks about his career as well as his passion for literature, jazz and classical music, and Spanish and Chilean wine. A full and active life outside of radiology helps him cope with stress and avoid burnout, Martí-Bonmatí said.
Further interviews with the winners of the 2021 EuroMinnies awards will follow during the second half of April. Now in its third year, the scheme is an annual event recognizing excellence in radiology. Candidates are nominated by AuntMinnieEurope.com members, with winners selected by an expert panel in two rounds of voting. A full list of winners in the 2021 edition of the EuroMinnies is available on AuntMinnieEurope.com.
![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.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![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.auntminnieeurope.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)
