While medical AI has been receiving significant attention recently, various examples and discussions show that serious limitations remain in actual clinical practice. Standard benchmark scores alone cannot be trusted to reflect medical AI's actual 'medical reasoning' ability, and real-world cases and rigorous validation are needed — that's the conclusion of this discussion. However, a balanced perspective acknowledging AI's useful supplementary roles is also presented.

1. Real Error Cases of Medical AI Models

Citing a recent @Microsoft paper, the discussion points out that medical AI models fail to properly perform meaningful reasoning in actual clinical situations.

AI models have an anchoring problem — they fail to correctly connect patient question text with medical images and over-rely on whatever image is presented. When a different image is shown, they quickly change diagnoses or arrive at incorrect ones.

"The model strongly anchors to whatever image is shown, and if you swap in a distracting image, it immediately abandons the correct diagnosis."

The image below illustrates these errors well.

2. 'Plausible but Wrong' AI Interpretation Errors

Another major problem is that AI confidently provides plausible explanations while actually repeating systematic errors. This is extremely dangerous in medical practice.

For example, when a model diagnoses from a chest X-ray, it may base its explanation on incorrect reasoning and ultimately deliver a diagnosis that 'sounds serious but is actually wrong.'

"The model confidently offers plausible explanations while actually repeating systematic errors. This is extremely dangerous in clinical settings."

The following image shows such a situation.

3. Distorted Evidence and Near-Fictional AI Reasoning

AI pretends to 'analyze' images, but in reality relies on non-existent details or reasons based on information that doesn't match the facts. This can lead to severely wrong conclusions, and such errors are highly controversial in clinical settings.

"Medical AI models appear to carefully analyze images, but their actual reasoning is based on inaccurate or near-imaginary details."

This case is also well illustrated in the image below.

4. Counterarguments on Latest LLM/Model Performance & Benchmark Limitations

Some criticize that recent researchers generalized their conclusions by only evaluating with standard GPT-5, not testing with the latest versions like GPT-5 Pro. They argue that the latest models have actually produced better diagnostic results in more realistic scenarios.

"We used actual patient data, and GPT-5 Thinking and GPT-5 Pro results differ from this paper's conclusions. We'll publish these results soon — it's simply a pity the authors generalized without using GPT-5 Pro."

However, many experts emphasize that passing 'medical school exams' doesn't mean being able to 'safely treat real patients as a doctor,' making much more rigorous real-world validation essential.

"Benchmarks are like medical school exams. Passing the exam doesn't mean you can save patients. More rigorous real-world validation is needed before trusting AI in clinical practice."

5. Proposals for Proper Medical AI Utilization and Reliability

Many argue that rather than current general-purpose LLMs, it's better in terms of accuracy to build models specialized for medical data and fine-tune them carefully. General LLMs easily make mistakes on items like rare genetic variants and can become dangerous by over-generalizing conclusions. On the other hand, ensuring diagnostic reliability requires explainable AI (Explainability, e.g., tools like SHAP).

"Medical data needs finely tuned models rather than general-purpose LLMs. AI decision processes must be explainable through tools like SHAP to be truly trustworthy."

However, if AI models are used for primary triage, medical image screening, and report draft generation, the value remains significant. As long as they're not the 'absolute standard for a single diagnosis,' they can serve as partners in the actual clinic.

"Not all progress is meaningless. For triage, image screening, report drafts — anything other than single definitive diagnosis — AI is sufficiently useful."

Lastly, whether a truly expert-grade model has the courage to say 'I don't know' is also important.

"If a model like GPT-4o could answer 'I don't know,' wouldn't that actually be safer for doctors to use?"

Another interesting study mentions that when the correct answer was replaced with 'none of the above' in 4-choice medical multiple-choice questions, LLM performance dropped by more than half (81% to 42%). This shows the reality that simple pattern matching doesn't translate to actual reasoning.

"When 'none of the above' was substituted for the correct answer in medical multiple-choice questions, LLM performance dropped by half."

Conclusion

Medical AI holds enormous potential in clinical settings, but currently still sits under systematic errors and limitations in training data/evaluation methods. The article emphasizes that validation reflecting actual clinical situations, ensuring explainability, and rigorous scope of use are prerequisites for medical AI trustworthiness and safety going forward, and a vigilant approach is necessary.