 I attended the “Deep Learning in Healthcare” conference put on by Rework in Boston on Thursday, May 23 and Friday, May 24. I can say it was one of the best conferences I have ever attended. The topics were relevant, challenging, innovative, and thought provoking. The sessions were only 15-20 minutes long. This allowed for more speakers to share and for the presentations to be succinct. Presenters stayed around the conference after their talks to answer questions and discuss further. The moderator kept the conference on time so all the speakers had their full amount of time. Overall, it was well run and exciting. There were a couple of quotes from speakers that stood out to me and summed up the current state of Deep Learning and AI in healthcare. In this post, I am going to discuss these quotes. I will give details of the other talks in a follow-up post.  Ajay Narayanan, CTO of mfine Quote 1: Ajit Narayanan, CTO of mfine, a company that developed a mobile app to help physicians and patients in India said, “There is approximately 1 physician per 5,500 people in India.” That’s the equivalent of having only 479 physicians for the entire population of Dallas county. In Dallas county alone, there were 5,924 physicians in 2015. The disparity is staggering. This statistic stood out to me because it demonstrated the amazing need that AI can potentially help with, not only in the US, but around the world. Physicians can be more accessible and more productive, thereby increasing the health of people who may not otherwise get it. Using AI to improve the world should be a significant goal of any project.  Anthony Chang, MD, MBA, MPH, MS, Pediatric Cardiologist, Founder of Artificial Intelligence in Medicine (AIMed) Quote 2: In a panel discussion titled “The Future of Healthcare – What Can We Expect?”, Dr. Anthony Chang, a pediatric cardiologist stated, “Computer vision in healthcare is easy overall and can be considered the low hanging fruit. The future needs to be in helping physicians individualize health plans and deliver precision medicine. It should use cognitive architectures and intelligence to assist physicians with decision making.” He continued and made the points that it is impossible for a physician to read a 200+ page patient medical record in the few minutes the physician has with the patient. He wants to see AI that can analyze the medical record and give direction on the prescribed treatment. Is there a certain condition the patient had in the past that is correlated with a condition the patient is likely to develop or that exacerbates the condition they are seeing the physician for? AI can take all the information in the patient record and analyze it quickly and in the context of the entire patient history as well as in the context of other patients with similar conditions. The physician could be presented with this information in a succinct manner and could use it to make more accurate diagnoses and more targeted treatment plans. I found this statement fascinating. Many, many talks at conferences are on using computer vision in healthcare, particularly in radiology and pathology. In those specialties, computer vision is critical. However, in many specialties, such as cardiology, it is not, and in psychiatry it is irrelevant. I have heard other physicians at conferences say similar things. When I asked what problem he would most like to see solved by AI, Dr. Daniel Rubin, a radiologist at Stanford University, said he would like a strong solution that predicts if patients will cancel their appointments. Cancelled appointments result in unproductive time for physicians and keep other patients from being seen because the time slot is designated as filled. Other physicians I have questioned have mentioned finding interactions between drugs and specific conditions that are not currently known but that can found by analysis of large numbers of patients. I think in some ways we may be pursuing the wrong avenues of AI research. We have to collaborate with physicians and solve problems they face in their practices daily. We, as non-physicians, can make assumptions about what will be helpful, but it may not be. AI in healthcare projects should be laser focused and done with strong collaboration with physicians or the project may end up being irrelevant or not useful in the end. If you have questions and want to connect, you can message me on LinkedIn or Twitter. Also, follow me on Twitter @pacejohn and LinkedIn https://www.linkedin.com/in/john-pace-phd-20b87070/ #artificialintelligence #ai #machinelearning #ml #aiforgood #rework #deeplearning #medicine #healthcare #aiinhealthcare #cardiology #mfine #aimed #stanford
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