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In the U.S., almost three-quarters of patients utilize five-star scale web ratings of physicians to guide them. But how much should you trust these online ratings?

CGM-LSM improves near-future glucose prediction with high accuracy and robustness, advancing diabetes management through large-scale sensor data analysis.

This panel at the Inaugural Responsible AI for Health Symposium focused on the integration of AI into clinical settings and electronic health record systems. The experts discussed key considerations for using AI tools in medical decision-making and highlighted the challenges involved in building the infrastructure needed for successful implementation.

At the Inaugural Responsible AI for Health Symposium, Dr. Cynthia Rudin, Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science, presented on promoting fairness through improving the interpretability of AI models.

At the Inaugural Responsible AI for Health Symposium, this panel centered on the discussion of metrics that can be used to measure the fairness, risk, and trustworthiness of AI models. Panelists noted the unanswered questions regarding the governing body that will ultimately be responsible for selecting, deploying, and integrating fairness metrics. Dr. Anjana Susarla elaborated on the risks of AI, stating that they are primarily derived from “us ignoring this interplay between the way we develop all these AI systems and [their] use.”

At the Inaugural Responsible AI for Health Symposium, Dr. Emma Pierson, assistant professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, delivered a presentation on the training and evaluation of AI models using diverse datasets.

AI/ML can improve healthcare but may worsen inequities. Leaders must ensure fairness with audits, benchmarks, policies, and education to support equity.

The annual Conference on Health Information Technology and Analytics (CHITA) sparks ideas for the not-so-distant future of high-tech health care delivery.

Why it matters: Artificial intelligence is rapidly transforming the health care industry, and we must be vigilant to ensure that these technological advancements bring equitable outcomes. The Artificial Intelligence for Health Equity (AIHE) series at Johns Hopkins University addresses this challenge, ensuring that AI integration in health care fairly meets the needs of underserved communities.

Johns Hopkins Carey Business School’s Center for Digital Health and Artificial Intelligence is pleased to announce that Accenture Federal Services has become its latest partner. Accenture Federal Services’ applied intelligence practice will bring valuable insights to guide and shape CDHAI’s research portfolio.