| Sumario: | Gain actionable insights into how machine learning, data analytics, and subgroup analysis can be applied to make clinical decision support (CDS) systems better at diagnosing and treating disease. In this report, Paul Cerrato, former editor of InformationWeek Healthcare, discusses the algorithms that healthcare executives, technologists, and physician leaders need to construct these diagnostic and therapeutic systems. To illustrate the power of AI, this report also delves into several cutting-edge IT initiatives at Mayo Clinic designed to improve diagnosis and management of clinical depression, cardiovascular disease, and cancer. Each of these projects can serve as the impetus for entrepreneurs to create similar products and services. Three primary takeaways from this report include: How to develop a better understanding of computer-enabled diagnostic tools to help clinicians address the misdiagnosis epidemic that now exists in medicine How advanced data analytics can radically transform CDS systems to provide more individualized diagnostic and treatment advice Why Mayo Clinic use cases will inspire industry innovators to create similar tools and help investors determine which projects have the greatest chance of succeeding
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