Current and Potential Applications of Ambient Artificial Intelligence
Abstract
Ambient clinical intelligence is a conversational AI application that integrates ambient voice sensing technology and virtual assistant function to automate and streamline visit documentation into the electronic health record (EHR) and data retrieval from the EHR by a physician during a patient's clinic encounter. The touchless voice-activated virtual assistant helps to decrease the administrative burden of physicians, enables better physician-patient interaction, and improves patient satisfaction and experience. This article examines various ambient AI applications across healthcare settings including Mayo Clinic's Ambient Warning and Response Evaluation system for ICU clinicians, which filters meaningful data from vast EHR data volumes, delivering real-time context-specific high-value clinical information to augment timely clinical decision support and prevent data overload. Additional applications discussed include use of ambient cameras for surgical skill evaluation through computer vision, AI-based systems using contactless ambient radio sensors for detecting Parkinson's disease and tracking disease progression through nocturnal breathing analysis, and ambient assisted living tools for improving quality of life for elderly and people with functional diversity including voice assistants and smart home solutions. The article also addresses challenges including privacy concerns regarding capture and sharing of large volumes of personal data without patient knowledge or informed consent, use beyond original intended purposes by third parties, and the critical need for explainability in ambient AI systems to clearly explain predictions and decision-making processes to end users. Ambient AI is positioned as a promising emerging technology in the very early phase of adoption with great potential to become mainstream over the next 10-15 years, leveraging ambient sensing technology and connected intelligence networks in the 5G era to transform healthcare delivery and enable intelligence-based medicine.
Clinical implications
Comprehensive review of ambient AI applications across multiple healthcare domains. Ambient clinical intelligence defined as conversational AI integrating ambient voice sensing and virtual assistant functions for EHR documentation automation and data retrieval during patient encounters. Primary benefits: decreased physician administrative burden, enhanced physician-patient interaction, improved patient satisfaction and experience through touchless voice activation. Mayo Clinic implementation: Ambient Warning and Response Evaluation system for ICU clinicians filters meaningful data from vast EHR data volumes, provides real-time context-specific high-value clinical information, augments timely clinical decision support, prevents data overload. Surgical applications: ambient cameras with computer vision enable surgical skill evaluation, facilitate timely feedback for skill refinement, improve technical efficiency, decrease complication rates. Disease monitoring: Yang et al. study demonstrated AI-based system with contactless ambient radio sensor detecting Parkinson's disease, predicting disease severity, tracking progression over time using nocturnal breathing patterns - ambient sensor system accurately identified Parkinson's patients from nocturnal breathing and assessed disease severity and progression. Ambient assisted living: tools improving quality of life for elderly and functionally diverse populations including voice assistants, smart home solutions for automated task performance for motor disability patients, medication reminders, cognitive impairment adaptations. Privacy challenges: various ambient sensors capture large volumes of personal/private data potentially making patients uncomfortable, risk of inappropriate data sharing without patient knowledge, data use beyond original intended purposes or by third parties without informed consent. Explainability challenge: ensuring ambient AI systems clearly explain predictions and decision-making processes to end users. Technology adoption timeline: ambient AI in very early adoption phase with potential to become mainstream over next 10-15 years, leveraging ambient sensing technology and connected intelligence networks in 5G era to transform healthcare delivery and enable intelligence-based medicine.