Teaching Radiologists for an AI-First World with Ali Tejani

In this episode of Rethink Imaging, Chris St. John sits down with Dr. Ali Tejani, a neuroradiology fellow at UCSF and a national leader in radiology education, imaging informatics, and AI literacy. Ali shares why radiology training programs must evolve now to prepare trainees for a future where AI is embedded in everyday clinical workflows. From launching an imaging informatics and business intelligence track during residency to co-directing national informatics education initiatives, Ali explains how radiologists learn best, why traditional didactics are no longer enough, and how podcasts, social media, and hands-on AI experiences are reshaping education. The conversation explores generational learning differences, the risk of deskilling, ethical AI use, and why being a critical, informed AI user matters more than knowing how to code.

Radiology education is at an inflection point.

Dr. Ali Tejani joins Rethink Imaging to explore how artificial intelligence, digital learning, and changing trainee expectations are transforming how radiologists are trained.

He argues that programs that fail to introduce AI education are doing trainees a disservice, not because radiologists must become engineers, but because they must understand how to safely, ethically, and effectively use AI in real clinical environments.

Ali reflects on his journey into radiology, his early passion for teaching, and how frustration with fragmented AI learning resources led him to help build structured curricula and national training programs. He breaks down why modern learners gravitate toward case-based learning, flipped classrooms, podcasts, and social media and why education must be personalized rather than monolithic.

The discussion also tackles harder questions: how to introduce AI early without deskilling trainees, how to create psychologically safe learning environments, and why educators must shift from “hot seat” teaching to collaborative peer learning. Ali emphasizes that AI education should focus on pitfalls, bias, automation risk, and workflow integration, so radiologists graduate ready to practice with or without AI support.

Ali closes with a call to action for both trainees and educators: stay curious, advocate for better resources, and ensure AI is placed in trainees’ hands early, within controlled environments that protect patient care while accelerating learning.

What You’ll Learn:

Why radiology programs must include AI education now
How AI will impact every radiology practice setting
Why being a critical AI user matters more than coding skills
How generational learning preferences are reshaping education
The rise of podcasts, social media, and micro-learning in radiology
How flipped classrooms and case-based learning improve retention
The risks of deskilling and how to introduce AI responsibly
Why psychological safety matters in radiology training
How social media can be used responsibly for medical education
What trainees and educators can do today to prepare for the future

Chapters:

[00:00] Why AI Education Is No Longer Optional
[00:21] Introducing Dr. Ali Tejani
[01:31] What Drew Ali to Radiology
[03:17] When Teaching Became a Core Passion
[06:43] Launching an Informatics & AI Track in Residency
[10:20] Generational Differences in Learning
[14:01] Digital Content, Podcasts & Micro-Learning
[16:25] National Imaging Informatics Course Trends
[19:20] Case-Based Learning vs Traditional Didactics
[21:26] Psychological Safety & Peer Learning
[23:23] Social Media in Radiology Education
[28:06] Separating Signal from Noise Online
[30:06] The Future of CME & Interactive Learning
[31:34] What Radiologists Really Need to Know About AI
[35:38] Avoiding Deskilling While Teaching AI
[38:54] Advice for Trainees & Educators
[42:27] Where to Find Ali & Final Thoughts