Artificial Intelligence Models in Biomedical Image-Based Measurements: Seminar Series: Implementation and Evaluation of AI in Real-World Clinical Settings

In this seminar, Peter Bajcsy, PhD, Project Lead at the National Institute of Standards and Technologies (NIST), will examine efforts to utilize AI-based biomedical image analyses for predicting the functions of retinal pigment epithelial (RPE) implants from absorbance images. 

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The deployment of Artificial Intelligence (AI) technology currently outpaces its rigorous test and evaluation. In bio-medical applications, AI-model image-based measurements can provide new quantitative measurements and predictions for diagnostic and treatment purposes, for instance, when assessing the quality of tissue engineering implants or using AI-enabled medical devices for automated diagnosis. In this seminar, Peter Bajcsy, PhD, Project Lead at the National Institute of Standards and Technologies (NIST), will examine efforts to utilize AI-based biomedical image analyses for predicting the functions of retinal pigment epithelial (RPE) implants from absorbance images. Following that, there will be discussion on the challenges of relying on AI-based analyses and approaches to mitigate the risks.

About Peter Bajcsy, PhD

Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign (UIUC) and his M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania (UPENN). He worked for machine vision, government contracting, and research and educational institutions before joining NIST in June 2011. At NIST, Peter has been leading efforts focusing on the application of computational science in metrology, specifically live cell, and material characterization at very large scales. Peter’s area of research is large-scale image-based analyses and syntheses using mathematical, statistical, and computational models while leveraging computer science fields, such as image processing, machine learning, artificial intelligence, computer vision, and pattern recognition. Peter has authored more than 50 papers in peer-reviewed journals, co-authored 10 books or book chapters, and more than 100 conference papers.