I will present new computer vision algorithms to learn complex morphologies and phenotypes that are important for human diseases. I will illustrate this approach with examples that capture physical scales from macro to micro: 1) video-based AI to assess heart function (Ouyang et al Nature 2020), 2) generating spatial transcriptomics from histology images (He et al Nature BME 2020), 3) and learning morphodynamics of immune cells. Throughout the talk I'll illustrate new design principles/tools for human-compatible and robust AI that we developed to enable these technologies (Ghorbani et al. NeurIPS 2020, Abid et al. Nature MI 2020). 

Precision medicine is an approach to patient care that considers individual differences in a patient’s genetic and molecular makeup to predict disease progression and optimize treatment response – and one of the greatest opportunities, and challenges, in modern cancer care. There is currently no known method to predict the metastatic potential of any cancer at early stages. We propose to address this critical barrier by creating a cancer digital twin, a digital replica of a cancer patient using state-of-the-art artificial intelligence (AI) techniques applied to 900,000 abdominal CT scans from a high-volume cancer center. Predicting metastatic progression at early stages would radically transform our approach to cancer treatment, but promises substantial implications for patients and society that must be considered. For example, would you want to know a dismal prognosis predicted by your digital twin? How would you act on this information – would you regard the choice as yours, or your fate as given by your twin? In a world where AI is increasingly biased, how do we ensure that AI doesn’t create further inequities? Dr. Simpson will present new work on the development of a cancer twin for predicting metastatic progression as well as provide some discussion of the social ramifications of such technology.

Machine learning advances in the past decade have revolutionized computer vision, and have since also made their way into medical imaging. Although many associate medical imaging with radiology first, clinical pathology might be more significantly impacted, at least in the short term by machine learning. Partly due to the simultaneous advent of machine learning in medicine and digital pathology, but perhaps mostly because in pathology achieving inhuman feats is surprisingly low-hanging fruit. In this presentation, I will give some examples analog to radiology, then illustrate some superhuman applications. Subsequently, I will highlight our current limitations and possible future research directions.

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