GPT-4o: A Multimodal Large Language Model Not Approved for Independent Medical Diagnosis or Treatment
GPT-4o is a multimodal large language model (MLLM) developed by OpenAI that can process text, images, and audio inputs to generate text outputs, but it should not be used as a standalone tool for medical diagnosis or treatment decisions—it may serve only as a clinical decision support aid under direct physician supervision. 1
What GPT-4o Is
GPT-4o represents an evolution from GPT-4, maintaining powerful language comprehension while adding true multimodal capabilities. Unlike its predecessor that only accepted images and text as input, GPT-4o enables any combination of text, image, and audio input and output 1. This advancement allows it to process diverse clinical data types simultaneously, which theoretically aligns better with how clinicians work—integrating multiple information sources rather than relying on single data modalities 1.
The model builds on the GPT lineage that began with GPT-1 in 2018 and progressed through GPT-3 (175 billion parameters) and GPT-3.5 (ChatGPT), incorporating reinforcement learning from human feedback to align outputs with human preferences and reduce toxic content 1.
Performance in Medical Applications
Diagnostic Accuracy
Recent evidence shows GPT-4o demonstrates strong performance in structured clinical scenarios:
Clinical case challenges: GPT-4o achieved 88.4% accuracy compared to 85.0% for Medscape respondents (majority vote) in 1,426 real-world clinical cases (P=.005), though the newer o1 model performed even better at 94.3% 2
Multimodal analysis: When cases included images (n=917), GPT-4o maintained 88.3% accuracy (P=.005), significantly outperforming physician consensus 2
Radiology extraction: For pulmonary embolism diagnosis extraction from radiology reports, GPT-4o achieved sensitivity of 1.0 and F1-score of 0.975, outperforming the Clinical Longformer baseline model 3
Critical Limitations in Real-World Use
Brain tumor diagnosis study reveals fundamental constraints: In a retrospective analysis of 239 pre-operative brain tumor cases, GPT-4o showed markedly poor performance when analyzing radiological images alone (32% overall accuracy, only 25% for benign tumors). Performance improved substantially to 76% final diagnosis accuracy only when provided with structured radiological findings and patient histories—not raw images 4. This demonstrates GPT-4o cannot reliably interpret medical images independently.
X-ray interpretation deficiencies: GPT-4o achieved only 74% accuracy in traumatic extremity x-ray interpretation, significantly lower than orthopedists (86%) and emergency medicine specialists (84%), performing comparably only to emergency medicine residents (76%) 5. The model's accuracy improved when dislocations were excluded, suggesting specific diagnostic blind spots 5.
Clinical Applications Where GPT-4o Shows Promise
Based on the 2025 systematic review of 270 studies, GPT-4o and GPT-4 demonstrate best performance in 1:
- Disease diagnosis support (among the most commonly used models)
- Text generation (medical reports, documentation)
- Textual question answering (remaining the main choice with best overall performance)
- Information extraction from clinical text
Critical caveat: Performance remains highly context-specific. GPT-4o excels in approximately 30 different clinical subtasks, but this versatility does not equal reliability across all medical scenarios 1.
Why GPT-4o Cannot Replace Clinical Judgment
Knowledge Cutoff and Hallucination Risk
GPT-4o's training data only includes information up to October 2023, making it unable to incorporate newer clinical evidence 6. The model also carries inherent hallucination risk—generating plausible but incorrect information—which poses direct patient safety concerns 6.
Retrieval-Augmented Generation (RAG) partially addresses this: A 2025 meta-analysis of 20 studies showed RAG implementation increased LLM performance by an odds ratio of 1.35 (95% CI 1.19-1.53, P=.001) compared to baseline LLMs 6. However, this enhancement requires additional infrastructure and does not eliminate the need for physician oversight.
Lack of Regulatory Approval
GPT-4o is not FDA-approved as a medical device for diagnosis or treatment. It remains a general-purpose AI tool without the validation required for independent clinical decision-making.
Performance Variability
The model's accuracy varies dramatically based on:
- Input format: Structured text vs. raw images (76% vs. 32% accuracy in brain tumors) 4
- Clinical specialty: Better in some domains than others
- Question type: Excels at structured scenarios but struggles with nuanced clinical reasoning
Appropriate Clinical Use Framework
If using GPT-4o in clinical settings, implement these safeguards:
Physician verification mandatory: Every output must be reviewed and validated by a qualified clinician before influencing patient care
Optimal input strategy: Provide structured radiological findings and clinical histories rather than raw images alone 4
Specific use cases: Limit to administrative tasks (documentation, report generation, information extraction) rather than diagnostic decision-making 3
Consistency checking: Run multiple queries (GPT-4o provided correct answers 5/5 times in only 86.2% of cases) 2
Avoid high-stakes decisions: Do not use for time-sensitive diagnoses (like acute PE or trauma) where errors carry immediate mortality risk 5, 3
Common Pitfalls to Avoid
- Overreliance on image interpretation: GPT-4o's visual analysis capabilities are substantially weaker than its text processing 4
- Assuming current knowledge: The October 2023 cutoff means missing recent guidelines, drug approvals, or emerging evidence 6
- Treating outputs as definitive: Even high accuracy rates (88%) mean 12% error rate—unacceptable for independent diagnosis 2
- Ignoring specialty-specific limitations: Performance in orthopedic trauma interpretation lags behind specialists 5
The Bottom Line for Clinical Practice
GPT-4o represents a powerful tool for augmenting clinical workflows—particularly for documentation, information synthesis, and structured question-answering—but it fundamentally lacks the reliability, accountability, and regulatory approval necessary for independent medical diagnosis or treatment decisions. Its role is strictly adjunctive: a sophisticated reference tool that requires the same critical evaluation you would apply to any clinical decision support system, with final judgment always resting with the treating physician. 1, 6, 2, 4
The evidence consistently shows that while GPT-4o can match or exceed physician consensus in controlled, structured scenarios, its real-world performance degrades significantly when faced with the complexity and ambiguity inherent in actual clinical practice—precisely where physician expertise becomes irreplaceable.