What you'll learn

  • Improve understanding about the fundamental concepts of AI

  • Appreciate the significance of some of the advances that have taken place in AI

  • Develop a more informed view on managing AI projects

  • Have a more nuanced view of bias, fairness, and ethics in modern AI

  • Anticipate the upcoming developments in AI with appropriate timelines

  • Accurately assess the current state of the technology for each of the major subfields of AI

Course description

The health care industry is in a productivity crisis. For the last half century, technology, agriculture and manufacturing corporations have outpaced health care’s innovation. However, today artificial intelligence offers a tool that can help medical doctors, administrators and other stakeholders break out of this crisis. It is estimated that if implemented correctly, AI could improve health outcomes by up to 40 percent and reduce treatment costs up to 50 percent by improving diagnosis, increasing access to care, and enabling precision medicine.2

This could lead to Artificial Intelligence (AI) and cognitive computing empowering patients, transforming the practice of medicine, and saving the health care industry over $150 billion by 2025.1

This growing role of AI in health care organizations can harness data already being collected to inform and improve clinicians’ decisions and service to patients. Hospital and system leaders can make informed systems decisions to improve process and performance Unfortunately, many leaders responsible for making these decisions don’t know where to begin in applying AI for the best outcomes. This is where AI for Health Care: Concepts and Applications comes in.

For health care professionals, this program will help you think like a data scientist. It takes a “zero-to-AI” approach, using Harvard faculty to introduce AI beginners to key foundational concepts. This course outlines health care-specific subtleties that arise and places AI in the larger health care systems context. Find out how AI can change the relationship between doctor and patient and learn key principles for implementing ethical AI for progress with AI for Health Care: Concepts and Applications.

Course faculty will use group discussions, active learning strategies, case studies, and master classes to explore such topics as AI creation, potential implementation challenges, business models for AI in health care, and the future of the field over the next 5 years. Additionally this course is designed to encourage networking among participants, fostering a long-term support system you can lean on after the program concludes.

Solving Health Care Challenges with AI

There are many ways AI is helping overcome long-standing health care challenges:

  • Diagnosis: AI is able to process complex images, like CT scans, along with health records to make an accurate diagnosis in near real-time. A 2019 study found that AI correctly diagnosed diseases 87% of the time when reviewing medical imaging, compared to 86% by health care professionals.3 By combining the AI skill set with that of clinicians, the rate of misdiagnoses goes down, also helping reduce physician overload and in turn improving productivity.
  • Precision Medicine: AI has played a substantial role in the emerging field of precision medicine, which defies the one-size-fits-all approach to health care. Precision medicine is heavily based in data, taking into consideration a patient’s behaviors, environment, genome, and medical history to develop a more personalized treatment plan. AI helps manage the massive data sets used to inform this approach, allowing clinicians to better understand the patient, provide more specialized care, and more efficiently target resources. This has ultimately been proven to better treat disease and improve patient care.
  • Prediction Models: By using prediction models, clinicians can identify how a patient compares to others with a similar diagnosis, helping calculate potential outcomes. For example, it can help when determining if a patient is at higher risk of death, may need extra support to prevent complications, or can be released from the hospital shortly.

However, evidence also shows AI is also involved with important risks such as algorithmic bias. As individuals who develop AI carry implicit bias and health care systems exist in societies with prejudice, these biases end up being reflected in algorithms. It is crucial to think proactively about bias when developing and implementing AI by taking strategic actions to minimize the risk of algorithmic bias to ensure AI is helping – not further harming – the communities it serves.

1 Artificial Intelligence in Healthcare Takes Precision Medicine to the Next Level, Frost & Sullivan

 From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare, Frost & Sullivan

3 A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis, The Lancet Digital Health

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