Guest blog post by Art Papier, MD
- VisualDx CEO and co-founder As large language models (LLMs) and generative AI rapidly integrate into healthcare and other industries, a critical question emerges: Are we at risk of losing essential cognitive skills? Could an increased reliance on AI lead to cognitive deskilling among healthcare professionals?
The Risk of AI-Induced Deskilling in Medicine
The concept of deskilling is not new. In aviation, avionics and computer-assisted flight controls have dramatically improved safety. The tradeoff—pilots relying more on automation—is justified by the overwhelming reduction in accidents. However, medicine is fundamentally different. Unlike aviation, where the parameters are clearly defined, medicine is filled with ambiguity and gray areas.
AI’s Strengths and Limitations in Medicine
AI has already proven highly reliable for binary diagnostic tasks, particularly in:
- Radiology and Imaging: AI algorithms can detect breast cancer in images with remarkable accuracy.
- Ophthalmology: Machine learning (ML) models have been shown to diagnose diabetic retinopathy better than ophthalmologists.
- Dermatology: AI is advancing melanoma detection.
However, medicine is not just about binary decisions. Many diagnostic processes require clinical reasoning, where the correct test must be ordered, differential diagnoses must be considered, and patient presentations can evolve over time. An AI model might excel at recognizing patterns, but it lacks the nuanced decision-making required in complex cases.
AI Should Guide and Teach—Not Replace
AI should enhance human judgment and medical expertise rather than replace it. In 1969, Larry Weed’s SOAP framework proposed a structured problem list in The New England Journal of Medicine, advocating for standardization in patient evaluations.
Expanding on Weed’s vision, we should use AI as a tool that guides and teaches. AI should be designed to enhance clinicians’ diagnostic reasoning rather than diminish it. If we fail to take this approach, we risk a future where overreliance on AI leads to diminished clinical competence.
The Future of AI in Medicine: Responsible Integration
The question is no longer whether AI will transform medicine, but how to integrate it responsibly. The goal should be:
- AI-powered decision support that strengthens clinical expertise
- Medical AI tools that reinforce diagnostic reasoning
- Ethical AI integration that prevents overreliance and skill degradation
By leveraging AI as a tool for medical education and enhanced diagnostics, we can ensure it strengthens rather than weakens clinical competence in an era where critical thinking in healthcare has never been more vital.
Contact VisualDx Today
Want to see how AI can enhance—not replace—your clinical decision-making? Contact VisualDx for a conversation about how our AI-powered tools can support better differential diagnoses, improve accuracy, and strengthen clinical reasoning.