In the News: Artificial Intelligence in Medical Research

This week, Cleveland Clinic launched The Center for Clinical Artificial Intelligence, focused on translating AI-based concepts into clinical tools, developing innovative clinical applications for AI and leveraging machine-learning technology to improve health care delivery. It joins a growing list of research institutions developing AI systems for healthcare. 

In this new roundup, we look at the growing use of AI in medical research and healthcare.

What is AI?

 “Artificial intelligence” broadly refers to computing technologies that act intelligently and resemble processes associated with human intelligence. That includes things like learning, adaptation, sensory understanding, and interaction.

Applications of AI can range from relatively simple (a Scrabble tile identifier, perhaps?) to complex (the infamous self-driving car). While flashy consumer tech gets a lot of attention, the medical research field is at the forefront of innovation in AI.

AI in Medical Research

Are visions of robot doctors beep-booping in your head? AI-enabled “ambient digital assistants” are in the works, but there aren’t any Dr. Robotos in practice—yet.

One of the most impactful uses of AI in medical research and healthcare are in data collection and analysis. AI can process huge amounts of data—particularly text, statistical numbers, and images—a lot faster than human beings. That makes it very useful for health system analysis, drug discovery, and even diagnostics.

A few areas expected to benefit from AI include:

  • Medical imaging analyses

  • Vaccine design

  • Genomics

  • Healthcare analytics

  • Public health

The NIH lists even more applications here.

The Ethics of AI in Health Research

Research is full of ethical and compliance concerns, and the research and use of AI is no different. A key challenge as AI continues to progress will be to ensure that it’s developed and used in a way that’s compatible with the public interest, while still driving innovation.  

A report from the Nuffield Council on Bioethics identifies these potential ethical concerns:

  • Inherent biases in data used to train AI systems

  • Protection of potentially sensitive data

  • Effects on the roles and skills of healthcare professionals

  • The question of who is responsible when it’s used to support decision-making

  • The potential for AI to be used for malicious purposes.

Additional Reading and Resources

For more detailed news in the world of health research AI, here are a few resources that caught our eye.

These Researchers Are Using Artificial Intelligence to Make a Better Flu Vaccine
”Is there a more precise way to fight the flu? The team at Berg, a Boston-based pharmaceutical startup, thinks so. Working alongside French pharma giant Sanofi, Berg is using artificial intelligence and machine learning to better understand the flu and, hopefully, find new ways to stop it.”

  1. Cleveland Clinic launches Center for Clinical Artificial Intelligence
    “‘Cleveland Clinic has formed the Center for Clinical Artificial Intelligence to translate AI-based concepts into clinical tools that will improve patient care and advance medical research,’ said Dr. Aziz Nazha, director of the new center and associate medical director for AI, in a prepared statement.”

  2.  Seeking ground rules for AI
    ”This week, at the New Work Summit, hosted by The New York Times, conference attendees worked in groups to compile a list of recommendations for building and deploying ethical artificial intelligence. The results are included here “

  3. Artificial intelligence and the future of medicine
    ”Two AI experts at Washington University School of Medicine in St. Louis discuss the best uses for AI in health care and outline some of the challenges for implementing the technology in hospitals and clinics.“

  4. Researchers say use of artificial intelligence in medicine raises ethical questions
    ”In a perspective piece, Stanford researchers discuss the ethical implications of using machine-learning tools in making health care decisions for patients.”