Emergency care is moving onto screens—and artificial intelligence is increasingly the engine behind it. A new systematic review in the International Journal of Medical Informatics maps how AI is being used in virtual emergency care, highlighting both promising gains (faster triage, smarter routing, better decision support) and persistent gaps in safety evidence, equity, and real-world deployment.
Why AI in “virtual emergency” is suddenly a big deal
Virtual urgent and emergency care has matured from a pandemic-era convenience into a strategic front door for many health systems. The drivers are familiar: overcrowded EDs, staffing shortages, rising acuity, and patient expectations shaped by consumer telehealth. What’s changed is the complexity of the work being asked of remote teams. Virtual emergency programs are no longer just “video visits for low-acuity issues.” They increasingly include paramedic-supported home assessments, remote patient monitoring, nurse navigation, and escalation pathways that can dispatch ambulances or coordinate direct-to-bed admissions.
That complexity is also what makes AI attractive. In theory, algorithms can help a virtual emergency clinician make faster sense of incomplete information, detect deterioration earlier, and route the right patient to the right setting—without turning every encounter into an administrative burden. But emergency medicine is also where mistakes are least tolerated. The cost of an AI-driven miss—say, a subtle stroke flagged as “non-urgent”—is far higher than a scheduling error in primary care.
What the review suggests AI is actually being used for
According to the systematic review by Ravi Shankar and colleagues, published July 1, 2026, in the International Journal of Medical Informatics, AI applications in virtual emergency care cluster around a few core functions. One is triage: tools that stratify risk, prioritize queues, or recommend next steps based on symptoms, vitals, or prior history. Another is clinical decision support—algorithms that can flag red flags, suggest differentials, or support ordering and referral decisions. A third is operational: optimizing staffing, predicting volumes, and helping systems decide who can be safely managed at home versus who needs ED-level evaluation.
Read between the lines, and the field is still in a “proof and pilot” era. Many of these tools work well in controlled settings or narrow cohorts, but fewer have robust validation across diverse populations and the messy reality of virtual encounters—variable lighting and camera quality, incomplete histories, language barriers, and missing vitals.
The unique risks of AI when the exam is mediated by a screen
Virtual emergency care has an inherent data problem: clinicians often have less objective information than in-person teams. Even with home devices, vitals can be absent, inaccurate, or delayed. That creates a temptation to lean harder on pattern-recognition systems trained on richer hospital data, where lab values and continuous monitoring are plentiful. If an AI model was developed on in-person ED data, it may fail quietly when applied to tele-triage inputs that are noisier and less complete.
Bias concerns also get sharper in virtual settings. Access to bandwidth, device quality, digital literacy, and private space varies widely; these factors can correlate with socioeconomic status and race, shaping both what information is available to AI and how confidently it makes a recommendation. The result can be a new kind of inequity: not only who gets care, but who gets correctly classified as needing urgent escalation.
Finally, virtual emergency programs are workflow-heavy by nature—handoffs to EMS, referrals to urgent care, follow-up pathways, prescriptions, and documentation. AI that generates recommendations without integrating into these workflows can create “alert fatigue at a distance,” where clinicians spend precious minutes adjudicating suggestions instead of treating patients.
What this means for clinicians: augmentation, not autopilot
For emergency physicians, nurses, and paramedic teams, the near-term value of AI in virtual emergency care is likely pragmatic: prioritization, documentation support, and early warning cues—not autonomous triage. The review’s focus on AI-enabled triage and decision support underscores a key principle: in emergency care, AI should reduce cognitive load and widen a clinician’s situational awareness, while leaving responsibility and final judgment with licensed professionals.
Health systems adopting these tools should demand more than accuracy metrics. They should ask: How often does the model change a disposition decision? In which subgroups does it perform worse? What happens when the AI and clinician disagree? And how is performance monitored over time as patient populations and care pathways evolve?
What this means for patients: faster access—if trust holds
For patients, AI-assisted virtual emergency care could mean shorter waits, clearer routing (self-care vs. urgent care vs. ED), and earlier detection of serious problems. It can also reduce the “bounce” effect—patients sent to the ED after a telehealth visit because the clinician lacks confidence without an exam.
But patient trust will hinge on transparency and outcomes. People will tolerate algorithmic support if it demonstrably improves speed and safety. They will not tolerate a system that feels like a gatekeeper designed to keep them away from in-person care. Clear communication—why a patient is being escalated or not, what data was used, and what symptoms should trigger reassessment—will matter as much as model performance.
Where virtual emergency AI goes next
The most important next phase is evaluation in the wild. As virtual emergency programs expand, AI tools will need prospective studies that measure clinical outcomes, not just agreement with clinician labels. Expect more focus on hybrid models that combine patient-reported data, device vitals, and longitudinal EHR context—alongside safeguards like uncertainty estimation, escalation triggers, and continuous auditing.
Longer term, virtual emergency care could become a “learning system” where triage pathways continuously improve as outcomes feedback arrives. If that happens, the competitive advantage won’t be a single triage model—it will be the governance, monitoring, and clinical integration that keep AI safe as conditions change. The systematic review in the International Journal of Medical Informatics is a timely marker: the tools are arriving, but the industry’s credibility will be determined by how rigorously it proves they help more than they harm.
Source: Shankar R, Wang L, Hoe HS, et al. “The role of artificial intelligence in virtual emergency care: a systematic review.” International Journal of Medical Informatics (July 1, 2026), as reported by the journal’s publication page: https://www.sciencedirect.com/science/article/pii/S1386505626001516