Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.

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Study Objective: To assess the feasibility of a noncontact radio sensor as an objective measurement tool to study postoperative recovery from endometriosis surgery. Design: Prospective cohort pilot study. Setting: Center for minimally invasive gynecologic surgery at an academically affiliated community hospital in conjunction with in-home monitoring. Patients: Patients aged above 18 years who sleep independently and were scheduled to have laparoscopy for the diagnosis and treatment of suspected endometriosis. Interventions: A wireless, noncontact sensor, Emerald, was installed in the subjects' home and used to capture physiologic signals without body contact. The device captured objective data about the patients' movement and sleep in their home for 5 weeks before surgery and approximately 5 weeks postoperatively. The subjects were concurrently asked to complete a daily pain assessment using a numeric rating scale and a free text survey about their daily symptoms. Measurements and Main Results: Three women aged 23 years to 39 years and with mild to moderate endometriosis participated in the study. Emerald-derived sleep and wake times were contextualized and corroborated by select participant comments from retrospective surveys. In addition, self-reported pain levels and 1 sleep variable, sleep onset to deep sleep time, showed a significant (p <.01), positive correlation with next-day−pain scores in all 3 subjects: r = 0.45, 0.50, and 0.55. In other words, the longer it took the subject to go from sleep onset to deep sleep, the higher their pain score the following day. Conclusion: A patient's experience with pain is challenging to meaningfully quantify. This study highlights Emerald's unique ability to capture objective data in both preoperative functioning and postoperative recovery in an endometriosis population. The utility of this uniquely objective data for the clinician-patient relationship is just beginning to be explored.

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Background Behavioral and Psychotic Symptoms in dementia (BPSD) are associated with negative outcomes including increased mortality and morbidity, increased cost of care and caregiver burnout. Newer technologies are leading to a greatly enhanced ability to monitor and predict these symptoms using passive sensing and sophisticated analytics that rely on signal processing and potentially machine learning. In this study we utilized a novel passive sensing approach supported by AI to demonstrate how highly specific behavioral phenomena can be detected in real time with a need for limited in‐person supervision and monitoring. Methods Emerald is a device developed at MIT for in‐home, non‐intrusive patient monitoring. The device transmits low‐power radio signals (100x lower power than WiFi) and monitors the reflection of these signals from people/their environment to map behavioral information. Signal data are uploaded to the cloud where customized machine learning algorithms process the data to extract gait speed, sleep patterns, spatial location, respiration Subject: We installed the device in the rooms of 13 adults with Dementia in an assisted living facility outside Boston. We measured the subjects’ behavior continuously for 3 months. Sensor data findings were compared to staff observations around specific behavior phenomena. Our goal was to demonstrate Emerald’s ability to accurately capture a range of BPSD Results Over the course of the study, we identified a range of behavioral phenomena using the sensor, that were verified by staff and validated by standardized behavioral measures. These represent positive and negative states of activation. The sensor also captured the impact of pharmacotherapy. Conclusion This study demonstrating the feasibility of an AI‐backed passive sensor‐based device to accurately phenotype a range of behavioral phenomena. Our initial findings demonstrate that this technology can identify movement‐based behavior symptoms in a manner comparable to the current standard for measuring behaviors.

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