Because this view is not at the core of medical practice, the implementation and use of information technologies lags far behind what we experience on the web as informed consumers, for example on Amazon and Netflix, and the educational program does not see automation as a near-term, and certainly not present, companion to the clinical decision-making process.
Cardiology Advisor: What are some of the major benefits that AI could offer the field of medicine?
Dr Kohane: First of all, let me stipulate that the smooth integration into a natural clinical workflow is essential. We should not wish to reproduce the effect of health information technology such as Electronic Health Records which takes doctors away from the already limited time they have with patients and distracts them from their core mission. But supposing that part is done right, then the benefits include, in no particular order:
- Clinical decision-making would be informed at every step by what is known broadly in medicine, from textbooks to up-to-date population analyses.
- Patients could get their entire assessment and plan automatically translated into patient language, to be revisited at will and often.
- The plans across multiple care providers could be automatically scanned for incompatibility and danger before the clinicians are even aware of the possibility.
- The entirety of the data volunteered by the patient and multiple questionnaires would be integrated into decision-making on a day to day basis–or even more frequently–rather than waiting for a call from a doctor or nurse or a clinic visit.
- Repurposing existing drugs for new indications could be inferred from the timelines of millions of patients undergoing various therapies for thousands of diseases.
Cardiology Advisor: What are the current implications of these developments for our clinician audience?
Dr Kohane: Just like taxis lost their guild advantage from medallions to the Uber disruption, we should expect AIM to disrupt those activities that are truly rote in medicine and yet for which we bill as if they are acts of higher-level “slow-thinking” cognition. From tracking growth to scanning X-rays to reviewing pathology slides, to estimating gestational age from an ultrasound to interpreting whole genome sequences for variants with clinical impact, AIM is going to change the value proposition of clinicians.
It therefore behooves us to start thinking about which parts of our practice are the most valuable to patients and payors. I would start with the value of our common sense and human empathy, but we all know some colleagues who are not superlative in that respect either. I do think there are many important activities that computers will have a hard time reproducing, but if we and our professional societies do not start thinking hard about what those are and investing in our human capital in those areas, then Dr Robot is going to come a lot faster than need be.
- Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA.2016; 316(22):2353-2354. doi:10.1001/jama.2016.17438.
- Kubota KJ, Chen JA, Little MA. Machine learning for large-scale wearable sensor data in Parkinson’s disease: Concepts, promises, pitfalls, and futures. Mov Disord. 2016;31(9):1314-1326. doi:10.1002/mds.26693.
- Karanasiou GS, Tripoliti EE, Papadopoulos TG, et al. Predicting adherence of patients with HF through machine learning techniques. Healthc Technol Lett. 2016;3(3):165-170. doi:10.1049/htl.2016.0041.
- Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410. doi:10.1001/jama.2016.17216.
- Beam AL, Kohane IS. Translating artificial intelligence into clinical care. JAMA. 2016;316(22):2368-2369. doi:10.1001/jama.2016.17217.
- Schwartz WB. Medicine and the computer—the promise and problems of change. N EngI J Med. 1970;283:1257-1264. doi:10.1056/NEJM1970012032832305.