The American Board of Artificial Intelligence in Medicine (ABAIM) has been launched to provide physicians and other healthcare professionals with education and credentialing for AI in healthcare.
Individuals who successfully complete a standardized exam developed by the ABAIM will receive board certification, according to the ABAIM. The not-for-profit organization said the certification will ensure that these individuals have an advanced understanding of AI, machine learning, and deep learning, as well as the tools to analyze the benefits and pitfalls of these technologies in real-world scenarios.
The ABAIM said it will offer a modular educational curriculum that will enable a wide range of healthcare professionals, including clinicians, executives, technicians, and nurses, as well as patients, data scientists, and information technology personnel, to become knowledgeable in the application of AI, machine learning, and deep-learning tools and technologies.
Ultimately, the ABAIM hopes to enable healthcare professionals to participate in the development, assessment, selection, and implementation of these tools. The ABAIM has received an unrestricted educational grant from Guerbet.
Dr. Orest Boyko, PhD, of the University of Southern California and Dr. Anthony Chang from Children's Hospital of Orange County in Orange, CA, formed the ABAIM and are co-chairing the board. Dr. Matthew Lungren, an assistant professor of radiology at Stanford University, also is a board member.
![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)
