High-performance Medicine: The Convergence of Human and Artificial Intelligence
By Eric J. Topol
January 7, 2019
(Selected text mentioning AiCure – full text available here).
Artificial intelligence and patients
The work for developing deep-learning algorithms to enable the public to take their healthcare into their own hands has lagged behind that for clinicians and health systems, but there are a few such algorithms that have been FDA-cleared or are in late-stage clinical development. In late 2017, a smartwatch algorithm was FDA-cleared to detect atrial fibrillation128, and subsequently in 2018 Apple received FDA approval for their algorithm used with the Apple Watch Series 4 (refs. 129,130). The photoplethysmography and accelerometer sensors on the watch learn the user’s heart rate at rest and with physical activity, and when there is a significant deviation from expected, the user is given a haptic warning to record an ECG via the watch, which is then interpreted by an algorithm. There are legitimate concerns that the widescale use of such an algorithm, particularly in the low-risk, young population who wear Apple watches, will lead to a substantial number of false-positive atrial fibrillation diagnoses and prompt unnecessary medical evalautions131. In contrast, the deep learning of the ECG pattern on the smartwatch, which can accurately detect whether there is high potassium in the blood, may provide particular usefulness for patients with kidney disease. This concept of a ‘bloodless’ blood potassium level (Fig. 2) reading via a smartwatch algorithm embodies the prospect of an algorithm able to provide information that was not previously obtainable or discernible without the technology.
Smartphone exams with AI are being pursued for a variety of medical diagnostic purposes, including skin lesions and rashes, ear infections, migraine headaches, and retinal diseases such as diabetic retinopathy and age-related macular degeneration. Some smartphone apps are using AI to monitor medical adherence, such as AiCure (NCT02243670), which has the patient take a selfie video as they swallow their prescribed pill. Other apps use image recognition of food for calorie and nutritional content132. In what may be seen as an outgrowth of dating apps that use AI nearest-neighbor analysis to find matches, there are now efforts to use the same methodology for matchmaking patients with primary care doctors to engender higher levels of trust133.
One study has recently achieved the continuous sensing of blood-glucose (for 2 weeks) along with assessment of the gut microbiome, physical activity, sleep, medications, all food and beverage intake, and a variety of lab tests134,135,136. This multimodal data collection and analysis has led to the ability to predict the glycemic response to specific foods for an individual, a physiologic pattern that is remarkably heterogeneous among people and significantly driven by the gut microbiome. The use of continuous glucose sensors, which now are factory-calibrated, preempting the need for finger-stick glucose calibrations, has shown that post-prandial glucose spikes commonly occur, even in healthy people without diabetes137,138. It remains uncertain whether the glucose spikes indicate a higher risk of developing diabetes, but there are data suggesting this possibility139 along with mechanistic links to gastrointestinal barrier dysfunction140,141 in experimental models. Nevertheless, the use of AI with multimodal data to guide an individualized diet is a precedent for virtual medical coaching in the future. In the present, simple rules-based algorithms, based upon whether glucose values are rising or falling, are used for glucose management in people with diabetes. While these have helped avert hypoglycemic episodes142, smart algorithms that incorporate an individual’s comprehensive data are likely to be far more informative and helpful. In this manner, most common chronic conditions, such as hypertension, depression, and asthma, could theoretically be better managed with virtual coaching. With the remarkable progress in the accuracy of AI speech recognition and the accompanying soaring popularity of smart speakers, it is easy to envision that this would be performed via a voice platform, with or without an avatar. Eventually, when all of an individual’s data and the corpus of medical literature can be incorporated, a holistic, prevention approach would be possible (Fig. 3).