![](https://healthvr.com/wp-content/uploads/2024/05/DALL·E-2024-05-15-19.33.12-A-modern-X-ray-room-with-a-digital-X-ray-image-displayed-on-a-monitor.-The-image-shows-a-foot-X-ray-with-highlighted-areas-indicating-various-diagnose-1024x585-1.webp)
As a medical professional, I recently explored the capabilities of the new model of ChatGPT-4o in evaluating X-ray images. This journey has been enlightening, highlighting both the potential and limitations of using general AI models in specialized fields like radiology.
🩺 My Experience:
Years ago, I sustained a complex injury involving fractures in four metatarsals and a Lisfranc injury. At the time, the ER doctor only identified a more obvious fracture of the third metatarsal, missing the extent of the injury. Intrigued by the advancements in AI, I decided to see how ChatGPT-4 would handle the same X-ray image.
📊 Findings:
- Inconsistent Diagnoses: Using the same X-ray image with slightly different prompts, ChatGPT provided varied diagnoses ranging from Lisfranc injury to different metatarsal fractures and even hallux valgus deformity.
- Limitations of General AI: These inconsistencies underscored that while ChatGPT is a powerful tool, it is not specifically trained for radiological analysis. The variability in its responses highlights the need for specialized AI in medical imaging.
🔬 The Power of Specialized AI:
- Accuracy and Validation: AI models specifically trained and validated on extensive datasets of medical images have shown remarkable accuracy and consistency. These specialized AI systems are designed to assist radiologists by providing reliable second opinions, reducing diagnostic errors, and improving patient care.
- Research Backing: Numerous studies have validated the efficacy of these specialized AI tools in clinical settings, proving their potential to enhance diagnostic processes.
🖼️ ChatGPT-4’s Analysis Images:
I shared the same X-ray image with ChatGPT-4 multiple times, using slightly different prompts each time. The results were fascinating but inconsistent:
- First Prompt: Identified a Lisfranc injury, focusing on a fracture of the second metatarsal base and widening between the first and second metatarsals.
![](https://healthvr.com/wp-content/uploads/2024/05/XrayChat4o1-1024x758-1.png)
- Second Prompt: Suggested a fracture of the proximal phalanx of the great toe, noting a clear fracture line through the bone.
![](https://healthvr.com/wp-content/uploads/2024/05/XrayChat4o2-1024x768-1.png)
- Third Prompt: Indicated a fracture at the base of the fifth metatarsal, describing it as a potential Jones fracture.
![](https://healthvr.com/wp-content/uploads/2024/05/XrayChat4o3-1024x767-1.png)
- Fourth Prompt: Diagnosed a hallux valgus deformity, highlighting medial deviation of the first metatarsal and lateral deviation of the proximal phalanx of the hallux.
![](https://healthvr.com/wp-content/uploads/2024/05/XrayChat4o4-1024x735-1.png)
These differing diagnoses for the same image underscore the variability inherent in using general AI models for specific medical tasks, further emphasizing the importance of specialized AI systems.
🤝 Human-AI Synergy:
While AI, including models like ChatGPT, can offer valuable insights, the expertise of trained radiologists is irreplaceable. The collaboration between AI and human professionals ensures comprehensive and accurate diagnosis, ultimately leading to better patient outcomes.
🌟 Looking Forward:
The integration of AI in healthcare, particularly in radiology, is a promising frontier. By leveraging specialized AI systems, we can achieve greater diagnostic precision and efficiency, revolutionizing patient care.
#AI #Radiology #HealthcareInnovation #MedicalImaging #ArtificialIntelligence #DigitalHealth #HealthcareTechnology #MedicalExperience
This article was originally published on vrforhealth