A talk hosted by the Rajpurkar Lab at Harvard which works on developing medical AI. These talks cover recent papers or topics in core AI / medical AI in a format targeted to those interested in the cutting edge of AI and its applications in medicine.

In this session, expert guest Adam Yala presents his work on Machine learning for Personalized Screening. Adam is finishing up his PhD at MIT, advised by Regina Barzilay, where his research has focused on designing modeling approaches that are robust to data-generation biases, offer safe-guards for clinical deployment and can adapt to diverse clinical requirements.

See:
- Yala, A., Mikhael, P.G., Lehman, C., Lin, G., Strand, F., Wan, Y.L., Hughes, K., Satuluru, S., Kim, T., Banerjee, I. and Gichoya, J., 2022. Optimizing risk-based breast cancer screening policies with reinforcement learning. Nature medicine, pp.1-8.
- Yala, A., Mikhael, P.G., Strand, F., Lin, G., Satuluru, S., Kim, T., Banerjee, I., Gichoya, J., Trivedi, H., Lehman, C.D. and Hughes, K., 2021. Multi-institutional validation of a mammography-based breast cancer risk model. Journal of Clinical Oncology, pp.JCO-21.
- Yala, A., Mikhael, P.G., Strand, F., Lin, G., Smith, K., Wan, Y.L., Lamb, L., Hughes, K., Lehman, C. and Barzilay, R., 2021. Toward robust mammography-based models for breast cancer risk. Science Translational Medicine, 13(578), p.eaba4373.
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