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Early signs that using AI helps tumor boards improve workflows and care decisions.
Multidisciplinary tumor boards rely on evidence to help make the best recommendations possible for patients. The 3 million peer-reviewed publications related to cancer and more than 250 U.S. Food and Drug Administration-approved oncology drugs make keeping up with the latest developments a virtually impossible task. Doctors would have to spend approximately 21 hours each day to keep up with new professional insights, according to one estimate.
This deluge of scientific evidence holds great potential in that it can help clinicians personalize care plans for patients. One of the ways clinicians collaborate to personalize care for patients is through tumor boards, which are groups of physicians and other healthcare professionals with different specialties that meet regularly to discuss cancer cases. But vast amounts of new information present a problem for tumor boards: How can they efficiently incorporate the most relevant evidence into their decision-making?
Artificial intelligence (AI) is emerging as a decision support tool for tumor boards. It combs through evidence and data to find the most relevant information for that specific patient case. Recent studies highlight how tumor boards are using AI to:
These are just some examples of the role AI can play with tumor boards. While it’s just the beginning of the journey, and there are still challenges ahead, this early evidence of progress is encouraging. When tumor boards harness the capabilities of AI, the scientific evidence shows they can help improve clinician workflow and personalized care for patients.
About the author: Jeffrey T Lenert, M.D., MBA, FACS, is associate chief medical officer of Oncology & Genomics at IBM Watson Health. Prior to joining IBM Watson Health, Lenert was medical director of the Breast Care Center and attending surgeon at Walter Reed National Military Medical Center in Bethesda, Maryland.
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