https://news.emory.edu/stories/2024/09/hs_ai_faculty_hires_13-09-2024/story.html
AI experts (L-R) Farzad Fereidouni, Saima Rathore, Ravi Parikh, Yun Wang, Michael J.A. Girard, Sundaresh Ram and Drew Williamson are the latest hires joining the Empathetic AI for Health Institute at Emory University. Their expertise spans fields such as AI-driven health care, biomedical imaging and oncology research.
In its inaugural year, the Empathetic AI for Health Institute (AI.Health) took major strides forward in its mission to power a revolution in medicine. AI.Health has ushered in a new era, transforming disease prevention, diagnosis, treatment and outcomes. Global thought leaders like Emory’s Anant Madabhushi are forecasting how AI will reduce disparities in health care access and quality.
This year, the institute added seven new faculty members to an existing roster of 15 health care leaders. These additions are part of Emory’s campus-wide AI.Humanity Initiative, which since 2022 has welcomed more than 45 new faculty using AI to serve humanity. Emory’s health care ecosystem will provide many of these leaders with the tools and support necessary to drive innovation for human health.
As AI and machine learning reveal new insights, 2024 has been a notable year for precision medicine research at Emory. With promising discoveries for improved treatment for patients with certain breast cancers, early detection of severe eye inflammation and predicting heart transplant rejection. These advancements highlight the promise of using AI to produce better, more equitable health outcomes.
“That is why the work that we are doing is crucial. We’re approaching it from a pluralistic, multifaceted perspective, thinking about how we improve health and optimize medicine for our patients,” says Anant Madabhushi, PhD, executive director of AI.Health.
AI.Health new faculty
We are pleased to welcome the following faculty, who were hired in 2024 to join the AI.Health Institute and Emory’s AI.Humanity Initiative:
Research focus: Prospective evaluations of AI decision support; human-machine collaboration; prognosis and prediction of outcomes in oncology; in silico trials and digital twins; patient-generated health data; wearables; trustworthy AI; AI policy and regulation
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