Artificial intelligence and emergency physicians in the USA: enhancing clinical excellence while preserving physicians’ judgment

Emergency medicine (EM) is one of the most demanding medical specialties in the USA because of its clinical urgency, uncertainty, and rapid growth.^[1] Every day, emergency physicians (EPs) in the USA manage a wide range of patients, ranging from minor illnesses and injuries to life-threatening conditions such as stroke, sepsis, myocardial infarction, and multiple injuries. These clinicians must rapidly obtain and analyze complex clinical information, make critical decisions under time constraints and considerable pressure, and coordinate care across multidisciplinary specialties. Healthcare systems face growing resource constraints as patient volumes continue to rise. As a result, EPs are under mounting pressure to provide high-quality care that is both efficient and safe. While these responsibilities define the clinical mission of EM, an important portion of physicians’ work is devoted not to patient care but to documentation.

Artificial intelligence (AI) has become one of the most widely used technologies in modern medicine. In EM, AI refers to computer programs that can perform many patient-care-related tasks, including disease-pattern recognition, diagnostic prediction, clinical decision support, and automated documentation. Mounting evidence supports the view that AI improves diagnostic accuracy, reduces physician workload, and enhances clinical efficiency. Further studies are needed to better understand algorithmic bias, patient privacy concerns, and the potential risks of excessive reliance on AI.

Experts in medical AI suggest that AI cannot replace EPs but can enhance physician expertise and improve patient care. Maintaining physician responsibility is essential to successfully integrating AI into EM, along with ensuring ethical implementation and preserving the human aspects of clinical care that technology cannot reproduce.

Challenges facing emergency physicians

Emergency departments (EDs) serve as the front line of the healthcare system. According to the Centers for Disease Control and Prevention, emergency departments in the USA see more than 130 million patient visits each year.^[2] Many of these visits involve complex medical conditions that require immediate diagnosis and treatment.

However, EPs face significant operational and workforce challenges every day. ED overcrowding remains a nationwide problem. ED patients commonly experience long waiting time, prolonged or delayed evaluation, and ED boarding while awaiting final disposition, which inevitably results in worse clinical outcomes and lower patient satisfaction.

EPs also face increasing administrative burdens. The widespread utilization of electronic health records (EHRs) has improved the ability to obtain and analyze patient information. However, EHRs have created substantial documentation requirements. Physicians often spend hours completing medical notes and entering orders. All these increased administrative tasks and time-consuming documentation contribute to physician burnout.

Burnout among physicians is a national concern. The National Academy of Medicine identified physician burnout as a systemic problem affecting the quality and safety of patient care and workforce retention.^[3] EPs are particularly vulnerable because of irregular working hours, high volumes of critically ill patients, and emotional and physical stress. Research by Shanafelt et al^[4] demonstrated that physician burnout rates increased significantly during and following the COVID-19 pandemic.

Healthcare leaders in EM must formulate strategies that improve the efficiency and safety of patient care. AI offers a promising tool for addressing many of these challenges.

Applications of AI in the clinical practice of EPs

Supporting clinical decision-making

Clinical decision support is one of the most important AI applications in EM. Under significant time constraints, EPs routinely make diagnostic and therapeutic decisions with limited and uncertain information. AI can rapidly retrieve and analyze large amounts of clinical data and identify patterns that are associated with specific diseases or outcomes.

Machine learning algorithms can retrieve information on laboratory values, vital signs, imaging, medications, and physician notes simultaneously.^[5] These systems may identify subtle trends that are difficult for clinicians to recognize in real time. For example, AI models can help detect patients at risk before overt clinical deterioration occurs.^[6-9]

One significant cause of preventable medical errors is misdiagnosis. EM practice is extremely time sensitive. Frequently, critical decisions must be made within minutes. These situations may increase the risks of missed or delayed diagnoses and resultant malpractice. AI decision-support systems can serve as an additional tool to help physicians consider a broader differential diagnosis. They can also identify subtle findings that might otherwise be overlooked.

AI-based systems cannot be used to replace physician judgment. High-quality patient care requires EPs with broad medical and ethical knowledge, in addition to clinical experience. AI algorithms can process data efficiently. However, they cannot mimic human experience. EPs remain pivotal for interpreting AI-generated information.

Improving diagnostic imaging

A core component of EM practice is reviewing and interpreting diagnostic imaging. EPs frequently rely on radiographs, computed tomography (CT) scans, magnetic resonance imaging (MRI), and ultrasounds to accurately diagnose acute illnesses.

Recent advances in deep learning have greatly improved the ability of AI systems to analyze medical images. Studies have demonstrated that AI algorithms can detect many emergency conditions with accuracy that is comparable to that of experienced radiologists,^[10] in fractures,^[11] intracranial hemorrhages,^[12] hypertrophic cardiomyopathy,^[13] and pneumothorax.^[14]

The clinical application of AI-assisted imaging interpretation is valuable in EM. In rural hospitals or hospitals in smaller communities, immediate access to radiologists may be very limited. AI-assisted image interpretation can rapidly screen and prioritize studies that reveal potentially life-threatening findings. These capabilities may reduce diagnostic delays and expedite necessary treatments for patients with critical illness. For example, AI can rapidly detect intracranial hemorrhage on a CT scan, allowing EPs to consult neurosurgeons promptly. Similarly, AI-assisted interpretation of chest radiographs may facilitate the early diagnosis of pneumothorax or pneumonia. In EM, these advances may improve patient outcomes.

EPs cannot rely solely on AI-based imaging algorithms. False-positive and false-negative results remain important limitations. Physician review with radiologist confirmation remains essential to reduce AI interpretation associated errors. AI should serve as an assistant to physicians rather than a substitute for physician judgment.

Predicting and detecting potentially lethal diseases

One of the fastest-growing applications of AI in EM is predictive analytics. Disease prediction models use historical and real-time data to estimate the likelihood of future clinical events. These tools can help EPs identify high-risk patients and intervene before they deteriorate significantly.

The value of predictive analytics is exemplified in sepsis. As sepsis is a leading cause of morbidity and mortality, the outcomes of patients with sepsis depend on prompt recognition and treatment. Unfortunately, early symptoms may be subtle, and clinical deterioration may be difficult to predict during the early stages of illness. AI systems can constantly monitor patient data and identify patterns suggestive of developing sepsis before traditional clinical criteria are met.^[15]

Predictive algorithms may identify patients who are at increased risk for hospital readmission and intensive care unit admission. They can also predict the likelihood of developing respiratory failure and even cardiac arrest. Early prediction and recognition of clinical deterioration will help EPs provide timely intervention, potentially improving outcomes and survival.

In addition to individual patient care, AI-based predictive analytics can strengthen operational management at the institutional level. Hospitals and emergency departments can use AI models to project emergency department volumes and optimize staffing schedules. As a result, all available resources can be used more effectively. Such applications are particularly valuable during seasonal surges, infectious disease outbreaks, or mass casualty incidents.

Reducing administrative burden and physician burnout

AI also affects physician workflow. EPs spend a considerable portion of their time on administrative tasks. Documentation requirements associated with EHRs often divert EPs’ attention away from direct patient care. Many physicians report spending nearly as much time interacting with computers as they do with patients.

AI-based ambient listening technologies provide physicians with the opportunity to complete patients' notes while interacting with them. During physician-patient communication, AI-powered documentation systems can summarize patients’ medical histories and generate clinical notes that may also assist with coding and billing.

AI may reduce clerical workload in the emergency department. First, it decreases the time that physicians spend on repetitive administrative tasks, which is one of the most frequently cited sources of physician dissatisfaction. Second, by minimizing the time spent on documentation, AI also allows EPs to focus more on interpersonal interactions rather than computer-based tasks. The physician-patient relationship is one of the crucial components of high-quality patient care. Effective communication with patients and their families improves patient trust, patient satisfaction, and shared decision-making.

Future challenges of AI in emergency medicine

Ethical and legal challenges

Despite its considerable promise in EM practice, the application of AI raises ethical and legal concerns.

One major challenge physicians face is AI algorithmic bias. AI systems learn from historical data that may reflect existing inequities within healthcare systems. If the data used to train AI underrepresent racial and ethnic minorities, women, older adults, or people living in underserved communities, these data-generated AI algorithms may not be equally accurate across the patients in these subpopulations.

Obermeyer et al^[16] reported that a widely used healthcare algorithm underestimated the healthcare needs of certain black patients. Such findings highlight the importance of ensuring diversity in training data and the ongoing evaluation of clinical AI tools for fairness and equity.

Another challenge is the transparency of AI-generated systems. Many machine learning systems provide predictive tools without clearly showing how those predictions are generated. Physicians are usually reluctant to accept any recommendations that they do not fully understand, particularly when patients’ lives are at risk.

The World Health Organization has advocated for AI systems that allow clinicians to understand algorithmic reasoning.^[17] Transparency is critical not only for physician acceptance but also for patient safety.

Additional concerns include data privacy and cybersecurity. AI systems require access to significant amounts of sensitive patient information. Healthcare organizations must ensure compliance with privacy regulations and implement robust security measures to protect against cyberattacks and data breaches.

Legal liability is another question that has yet to be satisfactorily answered. If an AI-generated recommendation results in patient harm, it will be very challenging to determine who is responsible. Therefore, clearly structured institutional policies are necessary to guide AI accountability.

Preserving the human role of emergency physicians

Public discussions about AI often focus on the potential replacement of the human workforce. In EM, however, such concerns are largely misunderstood.

To make accurate diagnoses and provide effective treatments, EPs often communicate complex medical information with patients and their families and collaborate with multidisciplinary teams. They also advocated for emotional support during crises and navigated difficult ethical shared decision-making. These tasks require empathy, judgment, and compassion that AI cannot replicate. An AI system may generate a diagnostic prediction, but it cannot comfort stressed patients and their families or explain the ethical complexities of end-of-life decision-making.

AI is unlikely to replace EPs; rather, its greatest value may come from strengthening physicians’ capabilities. Instead, safe and efficient emergency care will require close collaboration, in which clinicians and algorithms work together to achieve outcomes that neither could accomplish independently.

AI rapidly processes data and identifies disease patterns using large databases. Physicians interpret information from AI systems, understand human behavior, and apply ethical principles. Mindful integration of AI and physicians can create a more effective and higher-quality healthcare system.

Conclusion

AI offers useful tools for EPs in the USA. Its potential extends beyond improving diagnostic accuracy. AI may help physicians identify patients at risk for clinical deterioration and improve the use of limited emergency department resources. However, AI tools need to be validated in the settings where they will actually be used and monitored after implementation. Physicians and health systems must also consider patient privacy, potential bias, the transparency of algorithmic recommendations, and responsibility when an AI-supported decision is incorrect. The introduction of AI should strengthen, rather than diminish, the physician-patient relationship. The future of AI in emergency medicine should not be measured simply by how much work can be automated. Its real value should ultimately be judged by whether it enables emergency physicians to provide safer, more efficient, and more compassionate patient care.

Funding: None.

Ethical approval: Not needed.

Conflicts of interest: None declared.

Contributors: FX conceived the review, drafted and approved the submitted version.