Is Google’s Med-PaLM AI Really Better At Diagnosing Than Doctors?

In 2025, there is barely any industry that has not been affected or automated by Artificial Intelligence. While questions about ethics, bias, and job displacement remain, the benefits of AI tools are undeniable.

What Is Google’s Med‑PaLM AI?

How Does Google’s Med‑PaLM AI Work?

Med‑PaLM AI Vs. Human Doctors: What Do Studies Say?

• USMLE Exam Vs. Real-World Accuracy

  Med‑PaLM has so far nailed USMLE-style questions; however, such exams differ from real-life cases. A systematic review of 83 AI vs. clinician studies found that AI tools averaged only 52.1% accuracy, significantly below that of expert doctors. However, this accuracy rate is comparable to that of non-experts, suggesting that while Med-PaLM trails medical specialists, it can match general practitioners.

• Differential Diagnosis Studies

  The McDuff paper focused on Google's Med-PaLM 2’s ability to generate differential diagnoses, that is, a list of potential diagnoses ranked by likelihood for challenging medical cases. When compared against human physicians, Med‑PaLM outperformed human doctors in diagnosing real-life scenarios with a score of 35.4%, much higher than the doctors using the LLM to achieve 13.8% accuracy!

• Area-Specific Comparisons

  Med-PaLM’s imaging AI outperformed radiologists in breast cancer screening, reducing false positives by 6% and false negatives by 9%. The Med‑PaLM M also impressed in chest X-ray report tests, either matching or exceeding radiologists in 40% of cases. While radiology is more pattern recognition, this test shows that LLMs could write reports as well as human experts in most cases.

• Explainability And Clinical Trustworthiness

  Although MedPaLM models are promising, explainability remains a major limitation for medical AI models and LLMs. Unlike human doctors, Med-PaLM cannot justify its reasoning and diagnoses with clinical experience. In a Stanford University study, only 39% of MedPaLM 2’s long-form answers were rated as “fully aligned” with clinical reasoning by expert reviewers. This often hinders physicians from adopting AI tools and raises concerns about high-stakes decision-making in critical patient care. 

What Does This Tell Us About Med-PaLM?

• Efficiency And Access

  AI solutions such as Google’s Med-PaLM can help non-specialists triage and diagnose patients, especially in rural or underdeveloped areas, improving access to medical care while reducing costs.

• AI As A Mentor

  Tools like Med‑PaLM can mentor medical students or residents by offering instant feedback or simulating tricky cases. Remember how Dr. House would come up with fictional medical cases to test his team’s response to incomplete patient data or oddball cases?

• Backup For Specialists

  In complex medical cases, such as rare diseases, Med-PaLM can offer a discrete second opinion. This is not meant as a replacement for experienced medical professionals but simply a tool to broaden differential diagnoses. 

• Risks And Liabilities

  Despite being trained on millions of data points, Med-PaLM cannot replace the years of training that clinicians undergo. So, if an inexperienced doctor relies on an AI model’s incorrect diagnosis, who gets the blame?

• Ethical Biases

  Among the leading concerns is the lack of transparency and validation of AI diagnostics in patient care. A recent study flagged that 50–90% of LLM-based medical statements lacked full sourcing, leading to poor decision-making or biased interpretations.

Limitations Of Google’s Med-PaLM

• Context Understanding

  LLMs cannot fully process electronic health records (EHRs), social history, or nuanced clinical subtleties that add context about the patient's history. This severely limits the LLM's understanding of the patient’s comorbidities and previous illnesses, working only with present data.

• Lack Of Explainability

  AI models are prone to hallucinations, leading to opaque reasoning that can reduce trust from the medical community. Clinicians demand transparent logic, and Med-PaLM’s reasoning may not be fully transparent to them or the patient.

• Data Fairness

  Med-PaLM was trained on 200,000+ medical questions and 350,000 imaging scans. However, the training dataset must reflect diverse populations and demographics, as missing certain patient groups can lead to flawed diagnoses.

• Human-AI Collaboration

  Studies show doctors don't always benefit from AI solutions, sometimes performing worse than they would without the tool. This opens the need for effective interfaces that simplify and augment medical professionals’ workflows.

Conclusion