Responsible AI for Health, with Aparna Balagopalan
Aparna Balagopalan is a PhD student in the Department of Electrical Engineering and Computer Science (EECS) at @mit.
In this episode, we present the intersection of AI and healthcare. Aparna shares her research on developing fair, interpretable, and robust models for healthcare applications. We explore the unique challenges of applying AI in medical contexts, including data quality, collaboration with clinicians, and the critical importance of model transparency. The conversation covers both technical innovations and ethical frameworks necessary for responsible AI deployment in healthcare settings.
REFERENCES:
[00:00:27] Aparna's Google Scholar profile
(https://scholar.google.ca/citations?u...)
[00:12:04] Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
(https://journals.plos.org/digitalheal...)
[00:19:58] Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data
(https://www.science.org/doi/pdf/10.11...)
[00:26:19] LEMoN: Label Error Detection using Multimodal Neighbors
(https://arxiv.org/abs/2407.18941)
ToC:
[00:00:00] Introduction and Guest Background
[00:01:05] Transition from Industry to Academia
[00:04:42] Navigating Challenges in Research
[00:07:15] Daily Life of a PhD Researcher
[00:08:42] International Perspective in Research
[00:12:02] Machine Learning for Healthcare that Matters
[00:14:12] Data Quality and Bias in Machine Learning
[00:17:42] Importance of Collaboration with Clinicians
[00:19:55] Labelling and Annotation Challenges
[00:23:16] Multimodal Data and Label Errors
[00:30:06] Responsible Data Challenges in Healthcare
[00:33:39] Advice for Women in AI
[00:36:58] Future of AI in Healthcare
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