I used Claude Code to get a second opinion on my MRI

Limits of LLMs for Medical Imaging

• Many commenters strongly argue current general-purpose LLMs are not reliable for image-based diagnosis, especially complex 3D modalities like MRI.
• Radiology and MRI researchers note frontier models are not trained or validated for subtle medical image interpretation and tend to hallucinate plausible-sounding findings.
• Several references to work showing “mirage reasoning”: models confidently describe and reason about images that were never actually provided.
• Practitioners report benchmarking frontier models on real medical image datasets (e.g., otoscopy, pathology slides) and finding poor calibration and high-confidence errors.

Radiology & Imaging Nuance

• MRI details: common “2D” slice protocols with gaps vs true 3D isotropic scans; tradeoffs between resolution, speed, and motion artifacts.
• Ultrasound in orthopedics is useful for soft tissue and tendons but poor for bone and small calcifications; plain radiographs or MRI can detect calcifications that ultrasound misses.
• Radiologists stress that negative findings are always conditional on modality and technique; “no X” on ultrasound doesn’t mean “no X” absolutely.

AI as Second Opinion / Patient Tool

• Some users share positive anecdotes where LLMs helped spot misdiagnoses or suggest overlooked conditions/treatments, especially from text reports and lab data.
• Others report blatantly wrong or internally inconsistent results on their own MRIs/X-rays, or obvious failures like misreading chessboards and basic images.
• A recurring “best use” pattern: use LLMs to understand reports, generate questions, or surface guidelines and differential diagnoses, then discuss with a human clinician.

Trust, Self‑Diagnosis, and “AI Psychosis”

• Clinicians worry about patients treating AI outputs as authoritative, eroding trust and consuming scarce visit time to debunk confident nonsense.
• Multiple comments liken this to an amplified “Dr. Google” problem, with extra danger because the answers sound expert and personalized.
• Some describe a quasi-religious overconfidence in AI (“AI psychosis”), where contrary expert feedback is dismissed as lack of vision.

Healthcare System Problems & Incentives

• Many stories of misdiagnosis, conflicting opinions, overtreatment (e.g., unnecessary imaging, procedures, homeopathic injections), undertreatment, and rushed 5–15 minute visits.
• This drives patients toward LLMs out of frustration and lack of access, not just techno-optimism.
• Several argue AI could be powerful as an internal tool for doctors (guideline lookup, lead generation, checklists), but not as a stand‑alone diagnostician, especially for imaging.