When people think of AI in emergency departments, they usually think triage. And triage AI is important—I’ve written about it before. But the potential of AI in emergency medicine extends well beyond determining who gets seen first.

Let me walk through the broader landscape of ED AI applications, including some that are already in use and others that are emerging.

Sepsis Detection and Management

Sepsis is a leading cause of preventable death in hospitals. Early detection dramatically improves outcomes, but sepsis can be subtle in its early stages. Busy EDs, with high patient volumes and limited monitoring, sometimes miss early signs.

AI sepsis detection systems analyse vital signs, laboratory results, and clinical data to identify patients at risk before obvious deterioration. The best systems can provide four to six hours of additional warning compared to traditional criteria.

Several implementations are running in Australian hospitals now. The evidence is accumulating—these systems can improve time-to-treatment for sepsis. But they’re not magic. Alert fatigue is a real concern; systems with high false positive rates get ignored.

What makes ED sepsis AI work:

• Integration with ED workflows (not a separate system)
• Tuned sensitivity for ED populations (different from ward settings)
• Clear escalation pathways when alerts fire
• Nursing and medical staff training on response

What undermines it:

• Too many alerts overwhelming staff
• Alerts that don’t lead to action pathways
• Poor integration with existing processes

Stroke Identification and Workflow

Stroke treatment is profoundly time-sensitive. “Time is brain” captures the urgency—every minute of delay costs neurons. AI can help at multiple points in the stroke pathway.

Imaging AI. CT and MRI interpretation AI can identify stroke findings faster than waiting for radiologist review. When a patient presents with stroke symptoms, AI-flagged imaging can accelerate decisions about thrombolysis or thrombectomy.

Large vessel occlusion detection. AI specifically designed to detect large vessel occlusions (the strokes most amenable to thrombectomy) can identify cases that need immediate intervention.

Workflow optimisation. Beyond diagnosis, AI can help optimise stroke workflows—tracking patient progress, flagging delays, and ensuring time targets are met.

The evidence for stroke AI is strong, and it’s one of the clearest value propositions in emergency medicine. Minutes saved translate directly to patient outcomes.

Cardiac Risk Assessment

Patients present to EDs with chest pain constantly. Most don’t have acute coronary syndromes, but the consequences of missing one are severe. Traditional risk stratification uses tools like HEART score or TIMI risk score.

AI approaches can potentially improve on these:

Enhanced risk scoring. AI that considers more variables than traditional scores, potentially identifying high-risk patients that rule-based scores miss.

Troponin interpretation. AI analysis of serial troponin patterns, not just threshold values, to improve diagnostic accuracy.

ECG interpretation. AI analysis of ECGs for subtle abnormalities that might be missed in busy EDs.

This area is developing but not as mature as radiology AI. Evidence is accumulating, and some systems are in clinical use, but broad validation is still needed.

Imaging at the Bedside

Point-of-care ultrasound (POCUS) is increasingly common in EDs. Clinicians perform bedside ultrasound for everything from trauma assessment to central line placement.

AI can enhance POCUS:

Image acquisition guidance. AI that helps novice sonographers obtain better images by providing real-time feedback on technique.

Interpretation support. AI analysis of ultrasound images to identify findings—free fluid in trauma, pleural effusions, basic cardiac function.

Quality assurance. Automated review of POCUS studies to identify cases that warrant further imaging.

This is still early-stage, but as POCUS becomes standard in EDs, AI augmentation will follow.

Decision Support at Disposition

One of the hardest ED decisions is disposition: does this patient need admission, or can they go home safely? AI could potentially help by:

• Predicting short-term deterioration risk for patients being considered for discharge
• Identifying patients who could be managed in observation rather than full admission
• Flagging high-risk patients who might otherwise be discharged

This is high-stakes AI with significant liability implications. Evidence would need to be very strong before deployment. But the potential value—reducing both premature discharge and unnecessary admission—is significant.

Implementation Challenges in ED Settings

EDs present unique implementation challenges:

Volume and pace. EDs are fast-moving, high-volume environments. AI that slows clinicians down won’t be used, regardless of accuracy.

Diverse presentations. Unlike specialty settings, EDs see everything. AI trained for specific conditions needs to handle the full range of presentations that arrive.

Workforce variability. EDs are staffed by physicians, nurses, and other clinicians with varying experience levels. AI needs to support the full range.

Integration with movement. Patients move through ED zones. AI that works at triage might not be relevant in resuscitation, and vice versa.

Interruption tolerance. ED clinicians are constantly interrupted. AI that demands attention competes with everything else.

What I’d Prioritise

If I were advising an ED on AI adoption, I’d prioritise based on:

Evidence strength. Stroke AI and sepsis AI have the strongest evidence. Start there.

Workflow fit. Applications that integrate naturally with existing workflows over those requiring significant workflow change.

Time-critical conditions. AI value is highest where time matters most—conditions where minutes of delay affect outcomes.

Safety net applications. AI that catches things clinicians might miss (second reader, safety check) over AI that replaces clinical judgment.

The Human-AI Balance

Emergency medicine is fundamentally about clinical judgment under pressure and uncertainty. AI can support this but shouldn’t undermine it.

The best ED clinicians have developed intuition through experience—the ability to recognise sick patients, anticipate deterioration, and make rapid decisions with incomplete information. AI that enhances this intuition is valuable. AI that becomes a substitute for it is dangerous.

Training programs need to ensure emergency physicians and nurses develop strong clinical skills even as AI becomes more prevalent. The AI should be a tool, not a crutch.

---

Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.