What AI tool is most commonly used by physicians worldwide?

AI Tools Most Used by Physicians Worldwide

The evidence does not identify a single "most used" AI tool by doctors globally, but rather reveals that AI applications in medical imaging—particularly echocardiography, cardiac CT, and diagnostic imaging analysis—represent the most widespread clinical implementations currently deployed in healthcare settings worldwide. 1

Current Clinical AI Applications in Active Use

Medical Imaging Dominates Real-World Deployment

• Echocardiography AI tools perform automated segmentation, volumetric analysis of cardiac chambers, ejection fraction calculation, and automated disease detection, representing some of the most mature clinical applications 1
• Cardiac CT applications include automated coronary artery plaque quantification, calcium scoring, and fractional flow reserve computation, with favorable comparison to manual measurements in multiple studies 1
• Cardiac MRI (CMR) algorithms analyze structural and volumetric cardiac parameters, myocardial blood flow, and tissue characterization for sudden cardiac death risk prediction 1
• Diagnostic radiology AI for detecting intracranial hemorrhage on head CT and large vessel occlusions on CT angiography has been implemented for acute stroke diagnosis 1

Emerging Clinical Decision Support Tools

• Risk stratification algorithms identify patients at risk for emergency department visits, predict mortality in immunotherapy candidates, and flag individuals for targeted therapies 2
• Sepsis prediction models detect sepsis 3-40 hours ahead of traditional approaches, with meta-analysis showing 44% mortality reduction (RR 0.56,95% CI 0.39-0.80) when coupled with early intervention 3
• Cardiac arrest prediction systems in pediatric ICUs predicted arrest up to 50 minutes before onset in 91% of patients, compared to only 6% detection by clinicians 3

Conversational AI Tools: Limited Clinical Integration

ChatGPT and Large Language Models

While ChatGPT has generated significant interest, the evidence shows it functions primarily as an educational and documentation assistant rather than a direct clinical tool 4, 5, 6:

• ChatGPT scored significantly worse than Google Search for medical recommendations (PEMAT-P score 68.2% vs 89.4%, p<0.001), though it performed better for general medical knowledge questions (87% vs 78%, p=0.012) 6
• Medical fidelity concerns include contradictory information, insufficient solutions, and omitted clinical details that impact accuracy 4, 5
• Primary use cases identified include addressing knowledge gaps during rounds, building differential diagnoses, supporting documentation, and medical education—not direct patient care decisions 7

Critical Implementation Reality

The AI-Physician Partnership Model

AI tools do not replace physicians but rather work alongside them, as clinical cases rarely fall neatly into algorithm-predicted categories given the inherent uncertainty in medicine 1, 8:

• Physician oversight remains essential to ensure appropriate application and prevent bias when AI is applied to populations outside training datasets 8
• Randomized controlled trials in endoscopy (colonoscopy ADR improvement, EGD blind spot reduction) demonstrate AI's value as an assistive tool during procedures, not as autonomous systems 1

Validation and Performance Gaps

• External validation is mandatory before deployment, as proprietary AI systems have shown substantially poorer real-world performance than vendor-reported metrics 2
• Algorithm degradation occurs over time as patient demographics and clinical contexts evolve, requiring regular updates 2
• Few AI tools have demonstrated actual patient outcome improvements despite promising preclinical performance, highlighting the "AI chasm" between development and clinical benefit 2

Geographic and Specialty Considerations

• Imaging AI democratizes expertise by providing high-quality cardiac diagnosis in underresourced areas lacking specialist interpretation, particularly when combined with handheld echocardiography 1
• Emergency departments and general wards show higher beneficial effects from AI/ML predictions compared to ICUs, making these settings particularly valuable for deployment 3
• Alarm fatigue reduction through convolutional neural networks differentiating true from false monitor alarms (only 5-13% of bedside alarms are clinically actionable) improves resource allocation 3

Key Limitations to Widespread Adoption

• Reimbursement frameworks remain underdeveloped, potentially widening healthcare disparities rather than reducing them 2
• Interoperability standards between devices and electronic health systems need definition to enable data sharing 3
• Limited large, well-labeled datasets and labor-intensive annotation processes hamper robust system development 3
• Bias propagation occurs when algorithms are trained on biased data, requiring systematic detection and correction 2