Explainability strategies for downstream and upstream AI

Summary
In this episode of the AI Engineering Podcast Daniel Sodickson, Chief of Innovation in Radiology at NYU Grossman School of Medicine, talks about harnessing AI systems to truly understand images and revolutionize science and healthcare. Dan shares his journey from linear reconstruction to early deep learning for accelerated MRI, highlighting the importance of domain expertise when adapting models to specialized modalities. He explores "upstream" AI that changes what and how we measure, using physics-guided networks, prior knowledge, and personal baselines to enable faster, cheaper, and more accessible imaging. The conversation covers multimodal world models, cross-disciplinary translation, explainability, and a future where agents flag abnormalities while humans apply judgment, as well as provocative frontiers like "imaging without images," continuous health monitoring, and decoding brain activity. Dan stresses the need to preserve truth, context, and human oversight in AI-driven imaging, and calls for tools that distill core methodologies across disciplines to accelerate understanding and progress.

Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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• Your host is Tobias Macey and today I'm interviewing Daniel Sodickson about the impact and applications of AI that is capable of image understanding

Interview

• Introduction
• How did you get involved in machine learning?
• Images and vision are concepts that we understand intuitively, but which have a large potential semantic range. How would you characterize the scope and application of imagery in the context of AI and other autonomous technologies?
• Can you give an overview of the current state of image/vision capabilities in AI systems?
• A predominant application of machine vision has been for object recognition/tracking. How are advances in AI changing the range of problems that can be solved with computer vision systems?
• A substantial amount of work has been done on processing of images such as the digital pictures taken by smartphones. As you move to other types of image data, particularly in non-visible light ranges, what are the areas of similarity and in what ways do we need to develop new processing/analysis techniques?
• What are some of the ways that AI systems will change the ways that we conceive of 
• What are the most interesting, innovative, or unexpected ways that you have seen AI vision used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on imaging technologies and techniques?
• When is AI the wrong choice for vision/imaging applications?
• What are your predictions for the future of AI image understanding?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links

• MRI == Magnetic Resonance Imaging
• Linear Algorithm
• Non-Linear Algorithm
• Compressed Sensing
• Dictionary Learning Algorithm
• Deep Learning
• CT Scan
• Cambrian Explosion
• LIDAR Point Cloud
• Synthetic Aperture Radar
• Geoffrey Hinton
• Co-Intelligence by Ethan Mollick (affiliate link)
• Tomography
• X-Ray Crystallography
• CERN
• CLIP Model
• Physics-Guided Neural Network
• Functional MRI
• A Path Toward Autonomous Machine Intelligence by Yann LeCun

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0