Globally, primary care faces sustained growth in demand, increased patient complexity, and a workforce whose full-time equivalent growth has not matched demand, resulting in persistent access pressures and delays [1,2]. The COVID-19 pandemic accelerated the adoption of remote and digital access, including online consultations and reinforced strategic commitments to “digital front door” models within health systems, such as the National Health Service [3-5]. Online consultation submissions in England rose from approximately 2.7 million in October 2023 to a peak of 8.3 million in October 2025, as seen in Figure 1, highlighting the rapid and sustained growth of digital entry points into general practice (GP) [6]. Evidence also suggests that digital access is not equity neutral. A systematic review of inequalities in remote GP consultations found differential use by sociodemographic characteristics, with internet-based consultations more frequently used by younger, more affluent, and more educated groups, and noted that the impact of these inequalities on clinical outcomes remains uncertain [7].

In this context, artificial intelligence (AI)–enabled triage combines structured questions, red-flag pathways, and machine learning (ML) risk stratification with electronic health record (EHR) integration and clinician oversight to route patients more efficiently and potentially improve safety [8-10]. In this viewpoint, we distinguish between 3 related but conceptually distinct system types: symptom checkers, clinical decision support systems (CDSSs), and AI triage. Symptom checkers are patient-facing digital tools that provide health advice or triage recommendations directly to users, often without clinician oversight, and have been widely evaluated in consumer and emergency contexts [8]. CDSSs are clinician-facing tools embedded within clinical workflows or EHRs that support decision-making through alerts, risk scores, or guideline-based recommendations [9,10]. AI-enabled triage refers to digital systems that collect patient-reported information and generate urgency or routing recommendations (eg, self-care, routine review, urgent GP assessment, or emergency referral), with or without clinician oversight [11]. These systems may be embedded within online consultation platforms, patient-facing symptom checkers, or CDSS. Importantly, not all online consultation systems are AI enabled and not all AI-enabled triage systems function as stand-alone symptom checkers.

This viewpoint advances 3 linked arguments. First, we argue that triage in primary care is a safety-critical and equity-sensitive function, such that errors or delays can produce serious harm and unequal outcomes. Second, we show that the current evidence base for AI-supported triage is dominated by emergency department (ED), vignette, and retrospective studies, with little real-world or equity-stratified evaluation in GP. Third, we argue that AI triage operates as a sociotechnical system shaped by human behavior, workflows, and governance, meaning that algorithmic accuracy alone cannot guarantee safety or fairness. This viewpoint aims to outline a practical agenda for evaluating and governing AI-enabled triage in GP that integrates real-world safety outcomes, equity-stratified performance reporting, and sociotechnical implementation and surveillance. The intended audience includes GP clinicians and practice leaders, digital health and AI developers, evaluators and implementation scientists, and policymakers and regulators responsible for deployment and monitoring. Our contribution is to consolidate a practical, real-world evaluation and governance agenda for AI triage in GP that integrates sociotechnical safety (workflow and human factors), equity-stratified performance reporting (including an example of fairness), and postdeployment surveillance.

Although AI-enabled triage systems are promoted as equitable tools for managing primary care demand, the current evidence reveals a persistent equity blind spot, driven by underrepresentation, limited fairness measurement, and neglect of equity-focused monitoring [51].

Equity in digital health demands more than equal treatment; it requires fair opportunity to achieve safe, good outcomes, especially when baseline disadvantages exist. Equity reporting should use structured tools. The PROGRESS-Plus framework, developed by the Cochrane-Campbell equity methods group, was designed to systematically identify and report on equity-relevant factors in health research [17]. It extends the original PROGRESS (place of residence, race and ethnicity, occupation, gender, religion, education, socioeconomic status, and social capital) acronym with “plus” dimensions, including age, disability, and language [17]. The framework was created to help researchers illuminate disparities that might otherwise be masked in aggregated data and has since been widely applied in clinical trials, systematic reviews, and digital health evaluations.

Alternative frameworks, such as the health equity impact assessment tool, as seen in Textbox 1, are used prospectively to anticipate equity impacts before interventions are deployed [52]. The SIITHIA (Strengthening the Integration of Intersectionality Theory in Health Inequality Analysis) checklist provides structured criteria for identifying inequities in digital health [53]. More recently, the digital health equity framework extends this approach to digital interventions and multidimensional analyses [54]. In practice, these can be operationalized by setting thresholds (eg, sensitivity gaps ≤5% between groups), with breaches triggering model review and corrective action. Complementing this, algorithmic fairness metrics, such as equal opportunity (equal TPRs), equalized odds (matching both TPR and FPR), and calibration integrity, are critical for measuring subgroup performance and detecting systematic bias. Without these frameworks, inequities may go undetected beneath aggregated performance.

The frameworks presented in Textbox 1 allow AI triage to be evaluated across safety, implementation, and equity dimensions. Combining them enables a more comprehensive assessment than any single lens can provide.

Textbox 2 provides an illustrative example of how to report equal opportunity (true positive rate; sensitivity) across intersectional PROGRESS-Plus strata.

Several dimensions of bias have been documented or are anticipated in an AI-driven triage system, as mentioned subsequently.

The first dimension is age. Older adults are frequently underrepresented in development datasets, increasing the risk of atypical presentations or those with limited digital literacy [58,59].

The second dimension is language and ethnicity. NLP models are extremely sensitive to linguistic variation, dialects, multilingual input, or limited English proficiency. However, model evaluations rarely account for these, threatening safe triage in diverse populations [60].

Furthermore, broader AI research (outside primary care) shows that racial and ethnic biases in algorithmic systems persist [61]. For instance, algorithms that relied on health care cost as a proxy for illness systematically undertriaged Black patients due to unequal access-driven cost differences [60]. Similarly, AI in imaging frequently underdiagnoses emergent pathology in marginalized groups. Black women have shown significantly higher underdiagnosis rates in medical imaging models.

Telephone or digital triage evaluations suggest that low-income individuals, ethnic minority groups, and displaced patients experience worse outcomes, though quantitative data remain sparse [62].

Single-axis analysis (age or ethnicity) is insufficient. Intersecting vulnerabilities (eg, older adults from minority ethnic backgrounds with language barriers) can compound risk and increase mistriage. However, only a limited number of studies disaggregate safety performance by intersectional subgroups, leaving some of the most disadvantaged populations effectively invisible in assessments. Such analyses often lack power; therefore, findings should be treated as exploratory unless supported by large, multisite cohorts.

In this viewpoint paper, we argue that AI triage stands at a crossroad; it has the potential to improve safety and access in primary care but requires real-world evaluation, equity-focused monitoring, and sociotechnical governance. Figure 2 summarizes the proposed real-world evaluation and governance loop for AI triage in GP. Digital entry points feed into AI-supported triage and clinician workflow, producing safety and service-use outcomes that inform monitoring and governance (including equity dashboards, drift detection, and incident reporting). Governance outputs drive model and workflow updates, enabling continuous improvement.

To shift AI triage from a hypothetical promise to an equitable, safe reality in GP and primary care settings, we propose 5 interrelated priorities, as mentioned subsequently.

The first priority is real-world evaluations in primary care (prospective or retrospective). Current evidence is dominated by vignette experiments or ED contexts. Prospective, real-world evaluations—such as controlled interrupted time series or (cluster) randomized controlled trials (RCTs)—that assess patient safety outcomes (eg, delayed diagnoses and avoidable emergency use), workflow effects, and override behaviors in GP practice are urgently needed. Where randomization is infeasible, controlled interrupted time series with matched controls and prespecified safety outcomes can provide strong quasi-experimental evidence.

The second priority is equity-stratified performance reporting. AI triage systems should be evaluated through the equity lenses, such as PROGRESS-Plus and other fairness metrics. This means disaggregating performance (TPR, FPR, and calibration) by age, ethnicity, language, deprivation, and their multidimensional aspects to identify and mitigate differential risks. Without such reporting, disparities will remain hidden.

The third priority is causal evaluation designs. Observational signals are helpful, but causality demands rigorous designs. Interrupted time series around AI deployment and RCTs, where feasible (to attribute safety effects more definitively to AI interventions), and propensity score methods, instrumental variables, or target trial emulation, where RCTs are infeasible, should be conducted.

The fourth priority is postmarket surveillance and governance infrastructure. Effective governance is not optional. Organizations should adopt frameworks such as people, process, technology, and operations to ensure structured oversight across personnel, process, technology, and operations. AI triage requires continuous monitoring, with statistically principled detection of drift, subgroup miscalibration, and emerging hazards [63].

The fifth priority is human-AI collaboration and implementation research. Research should shift from algorithm-centric evaluation to sociotechnical integration. Studies should examine how clinicians interpret, override, or trust AI suggestions; how AI supports (rather than disrupts) workflow; and how organizational culture shapes safe AI adoption. Mixed methods research combining qualitative insights with quantitative safety metrics will be critical.

Prospective evaluations in GP are challenging but feasible. Cluster-randomized trials and interrupted time series require careful handling of contamination between clinicians, cointerventions during rollout, seasonal and demand shocks, and variation in practice digital maturity. Outcome measurement also depends on data linkage (eg, EHR, urgent care, ED attendance, and diagnostic follow-up), and governance processes can slow implementation. Although such designs remain underused for AI-enabled triage, they are well-established in evaluating complex service interventions in primary care and are methodologically appropriate for this context [64,65].

AI triage offers potential for improving primary care efficiency, safety, and consistency, but current evidence leaves critical gaps. Without intersectional, real-world safety evaluations, implementation is not just uncertain; it may be ethically risky and may inadvertently magnify existing health inequities. Over the coming years, this field must commit to responsible, equity-focused, system-aware evidence generation. That means embedding AI evaluation within the disordered realities of practice, governance mechanisms that ensure fairness and transparency, and human-AI systems that augment care rather than add workload. Operationally, this requires three commitments: (1) prospective, real-world evaluations in GP; (2) equity-stratified performance reporting guided by frameworks, such as PROGRESS-Plus; and (3) rigorous postmarket surveillance with drift and subgroup monitoring and WHO International Classification for Patient Safety–aligned incident reporting. Without these, deployment risks amplify inequities rather than reducing them. With these commitments, AI triage can better deliver on its potential of safer, more equitable primary care.