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Stanford University BIODS388: Stakeholder Competencies for Artificial Intelligence in Healthcare

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Course Description Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.


Artificial Intelligence Trends in Healthcare

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Healthcare is emerging as a prominent area for artificial intelligence research and applications. Image recognition is revolutionizing diagnostics. Pharma companies are experimenting with deep learning to design new drugs. In the private market, healthcare AI startups have raised $4.3B across 576 deals since 2013, topping all other industries in AI deal activity. AI in healthcare is currently geared towards improving patient outcomes, aligning the interests of various stakeholders, and reducing healthcare costs.


Machine Learning in Healthcare: Defining the Most Common Terms

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To respond to the fluid nature of users' understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is "best" rather than "right". They provide machine-aided serendipity by wading through massive collections of diverse information to find patterns and then apply those patterns to respond to the needs of the moment. Their output may be prescriptive, suggestive, instructive, or simply entertaining.


Artificial Intelligence and Machine Learning in Healthcare. Part 3 of 7

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This session took place in February 2016. Part 3 of 7 - Speaker: Dr Matthew Howard, IBM Global Business Services, Watson Health Data mining, machine learning and artificial intelligence are becoming the most talk-about topics in digital health. With vast volumes of medical data available, exploiting these techniques to derive valuable insights may both challenge and reshape certain elements of our healthcare system. These new approaches are leading to redefining drug discovery, assisting and automating diagnoses and helping predict and prevent diseases using health record data – or even our digital footprint. But there remains much hype, confusion and misunderstanding in the field.


How machine learning is supporting healthcare

Huffington Post - Tech news and opinion

As such, it's a significant burden on the healthcare system, with estimates suggesting it costs $18 billion in the United States alone. In a bid to improve the diagnosis of the condition, a team from Zebra Medical Vision and the Clalit Research Institute set out to develop an algorithm capable of calculating bone density simply from looking at CT scans that are often produced for other purposes. In other words, patients can be tested for osteoporosis risk without having to undergo a specific procedure for it.