ChatGPT Cannot Reliably Interpret ECGs in Clinical Practice
ChatGPT-based AI models currently lack the accuracy and reliability needed for clinical ECG interpretation and should not be used without physician oversight. Recent direct testing of ChatGPT for ECG interpretation shows cardiologists significantly outperform these models (92.52% vs 57-63% accuracy), with particularly poor performance on complex cases and critical findings 1.
Current State of ChatGPT for ECG Interpretation
Performance Limitations
ChatGPT demonstrates inadequate accuracy across multiple studies, with correct ECG interpretation ranging from 57-63% compared to cardiologist accuracy of 92.52% 1.
Critical diagnostic failures occur frequently, particularly in assessing T waves (kappa = 0.048) and ST segments (kappa = 0.267), which are essential for identifying life-threatening conditions like acute coronary syndromes 2.
Complex cases pose significant challenges, with ChatGPT correctly interpreting only 70% of complex physician-to-physician consultation scenarios, compared to 100% accuracy on straightforward questions 3.
False positive rates are problematic, as ChatGPT classifies significantly more patients as at risk for Major Adverse Cardiac Events (MACE) than actually identified by physicians, potentially leading to unnecessary interventions and healthcare system burden 2.
Distinction from Medical-Grade AI
It is crucial to distinguish ChatGPT from purpose-built AI-ECG algorithms, which represent fundamentally different technologies 4:
Medical-grade AI-ECG systems using deep learning convolutional neural networks have demonstrated clinically meaningful performance, with some achieving AUC of 0.92 for detecting left ventricular dysfunction and improving first detection by 32% over usual care in prospective trials 4.
FDA-cleared AI-ECG algorithms exist for specific applications including rhythm classification, cardiac amyloidosis detection, and left ventricular dysfunction screening 5.
ChatGPT is a general language model, not a specialized medical diagnostic tool trained on ECG waveform data 1, 2.
Evidence-Based Guidelines on Computer ECG Interpretation
Established Standards for AI-Assisted ECG Analysis
Computer-assisted ECG interpretation should only serve as an adjunct to physician interpretation, never as a replacement 4:
All computer-based ECG reports require physician overreading regardless of the algorithm used 4.
Computer algorithms demonstrate variable accuracy (0-94% correct classification), with arrhythmias being the most problematic diagnosis 4.
For STEMI detection specifically, computer-assisted interpretation can be used as an adjunct given high specificity, but should not be used alone to rule out STEMI due to poor sensitivity and considerable false-negative risk 4.
Clinical Context Requirements
The American Heart Association emphasizes that AI/ML algorithms can scale expert capabilities but require proper implementation 4:
Rules-based interpretation in existing devices has known limitations that may adversely affect medical decision-making 4.
AI/ML algorithms may better mimic expert interpretation in early studies, yet widespread adoption and clinical validation data are currently lacking 4.
Implementation requires strong initial education programs, quality assurance programs, and ongoing oversight 4.
Specific Limitations of ChatGPT for ECG Interpretation
Performance by Complexity Level
Simple cases: ChatGPT shows better performance but still inferior to cardiologists across all difficulty levels 1.
Intermediate cases: Significant accuracy gaps emerge, with statistically significant differences from cardiologist performance (p < 0.05) 1.
Complex cases: ChatGPT correctly interprets only 70% of cases requiring specialist consultation, providing incomplete, inconclusive, or inappropriate recommendations in 30% 3.
Diagnostic Category Performance
Cardiologists significantly outperform ChatGPT in critical diagnostic categories 1:
Arrhythmia detection: Substantial performance gaps exist, though not always statistically significant.
Cardiac structural disease patterns: Cardiologists demonstrate superior accuracy.
Normal ECG patterns: No statistical difference between cardiologists and AI models, but this represents the easiest diagnostic category.
Clinical Implications and Recommendations
Current Clinical Practice
Physician interpretation remains the gold standard 4:
Competency requires interpretation of 500 ECGs under supervision according to ACC/AHA consensus guidelines 4.
Maintenance of competency requires reading 100 ECGs yearly 4.
Even expert cardiologists show intra-interpreter variability, highlighting the complexity of ECG interpretation 4.
Risk of Clinical Errors
Using ChatGPT for ECG interpretation poses specific patient safety risks 1, 2:
Missed critical diagnoses in ST segment and T wave abnormalities could delay treatment for acute coronary syndromes.
Excessive false positives for MACE risk could lead to unnecessary invasive procedures, increased healthcare costs, and patient anxiety.
Variable performance across patient demographics (though some improvement noted with female patients, differences not statistically significant) 1.
Future Directions and Current Limitations
Technological Gaps
ChatGPT lacks fundamental capabilities required for medical-grade ECG interpretation 6, 5:
No direct waveform analysis: ChatGPT processes text descriptions rather than analyzing actual ECG waveforms.
Absence of specialized training: Unlike purpose-built AI-ECG models trained on millions of ECGs with expert annotations, ChatGPT has general medical knowledge without specialized ECG pattern recognition.
No regulatory clearance: ChatGPT has not undergone FDA evaluation for medical device classification, unlike approved AI-ECG algorithms 5.
Improvement Over Time
While ChatGPT shows improvement between versions (74% to 92% accuracy on trivia questions, 50% to 70% on complex cases), this remains insufficient for clinical use 3:
Trivia question performance does not translate to real-world diagnostic accuracy.
Complex case performance remains well below acceptable clinical standards.
Unpredictable updates to the underlying model may change performance characteristics without clinical validation.
Common Pitfalls to Avoid
Do not use ChatGPT as a substitute for:
Emergency ECG interpretation where time-sensitive decisions affect mortality (e.g., STEMI, life-threatening arrhythmias) 4, 2.
Screening or diagnostic decisions without independent physician verification 1, 2.
Teaching tool for trainees learning ECG interpretation, as incorrect interpretations may reinforce errors 4.
Do not confuse ChatGPT with:
FDA-cleared AI-ECG algorithms that have undergone rigorous validation for specific clinical applications 5.
Computer-assisted ECG interpretation built into commercial ECG machines, which while imperfect, are designed specifically for this purpose 4.
Remember that even purpose-built AI-ECG systems: