Jun 19 2017

It’s Benign!

My surgeon Dr. Shawna Willey walked into the patient exam room where I waited nervously. I first saw her thumbs up before her beaming face. I could breathe again!

My friends and I who recently turned 40 and starting our baseline mammograms can’t help but wonder about the lack of consensus on optimal cancer screening strategies, target populations, its benefits and harms. My colleague Dr. Jeanne Mandleblatt and her team have studied breast screening strategies for decades and have shown that biennial screening from ages 50-74 achieves a median 25.8% breast cancer mortality reduction whereas annual screening from ages 40-74 reduces mortality an additional 12% but introduces very high false positive rates. Many women and their families are subject to extreme anxiety due to the sheer number of repeat mammograms, false-positives, benign biopsies and in 7% of the cases an over diagnosis.

My experience and of my friends with breast cancer screening are raising many questions. How can we better predict the target risk population who must undergo screening early and often? Would this decision-making process consider risk factors, lifestyle, and patient preferences? How often are patients with a diagnosis of a benign breast condition on a stereotactic core needle biopsy upgraded to a non-benign diagnosis on an excisional biopsy which requires full sedation and surgery? What was the care journey like for other patients like me – Asian female, healthy, no family history? How many in the US and globally have access to the excellent care and follow-up that I was privileged to receive from Dr. Willey and her expert team?

Touted as the fourth industrial revolution, Artificial Intelligence is poised to empower clinicians, patients and researchers in answering these questions. What is AI? The term was coined by Dartmouth professor Dr. John McCarthy in 1956 and defined as “the science and engineering of making intelligent machines, especially intelligent computer programs.” Applications of AI in medicine have been limited by the complexity of highly cognitive processes such as making a medical diagnosis or selecting a treatment which require integration of thousands of datasets with millions of variables and multiple interactions between these variables. It takes years to collect, organize and publish practice changing results such as Jeanne’s screening study. What if we could use data that we routinely collect during the care process and effectively use AI to assist clinicians in real-time to make informed treatment decisions?

Interested in learning more about AI in Biomedicine? Want to engage with expert scientists and product developers in AI? Register for Georgetown’s Big Data in Biomedicine symposium on October 27th!

Companies like Google and Amazon are betting big on this. Jeff Bezos wrote “..it is hard to overstate how big of an impact AI will have on society over the next 20 years”; Google’s Sundar Pichai, when asked recently about the next big thing at Google responded “I can’t quite tell exactly but advances in AI and machine learning, we are making a big bet on that and this will bring a difference in many many fields”.

We cannot have a conversation about AI in medicine without discussing IBM Watson, the supercomputer that sifted through 20 million cancer research papers, and conducted a differential diagnosis on a difficult to treat leukemia patient in 10 minutes by combining genomic data with the power of cognitive computing. One concern that informaticians including my informatics mentor Dr. Bill Hersh have raised is that the publicity around Watson has mostly been from news articles and press releases, primarily from researchers at IBM and call for a more scientific analysis, not n-of-one case reports, of its abilities in clinical decision making. Systems like Watson will benefit from systematic expert knowledge input to guide the cognitive computing processes in navigating the complex medical pathways.

While still early, AI is already starting to make important contributions to Medicine says AI professor at MIT and a recent breast cancer survivor, Dr. Regina Barzilay. She and her team are asking all the right questions of data – “can we apply the sophisticated algorithms we use to predict customer’s shoe-buying habits to adjust treatments for cancer patients?” “Can computers detect signs of breast cancer or even pre-malignancy earlier than humans are currently capable of?” And the Holy Grail – “Can we use the huge quantities of data from smart toothbrushes, wearables, genomic sequencing, medical records to get to the first and right treatment?”

What next?

In the last decade, big data in biomedicine has focused on collecting (e.g. through mobile and other IoT) and organizing (e.g. cloud computing) information but all signs point in one direction for the next decade – real world applications of AI. We will witness the development of expert systems, question-answering systems and deep learning methods that begin to address complex real world problems in medicine. These will augment, not replace, human expertise. Winners will find ways to rapidly and accurately integrate human input with computational output. Usability of these tools by end users and human factors will be key.

While a true tech automation enthusiast at heart and practice, I will never forget Dr. Willey’s kind and soft words as she clearly explained my pathology report. She also carefully noted in my medical record the rare chlorohexidine pre-op antiseptic agent hypersensitivity that I had developed post anesthetic induction.

One more data point!

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