AI vs. Doctor Diagnosis: It's About Collaboration, Not Competition
Patients are increasingly arriving with an AI-powered medical diagnosis and treatment plan, ready to debate differentials before you’ve opened their chart. Meanwhile, AI is working quietly in the background, flagging imaging abnormalities, ranking triage priorities, and pulling guideline-based recommendations into the workflow. As these tools shape more of the diagnostic conversation, the question becomes: Are we headed toward a future of AI vs. doctor diagnosis?
We asked Dr. Adam Rodman, to help us explore this trend and dispel myths. Dr. Rodman is a general internist at Beth Israel Deaconess Medical Center and an assistant professor at Harvard Medical School. His research focuses on how AI and human clinicians can work together to take better care of patients.
As Rodman sees it, the goal isn’t to replace human judgment, but to understand how to combine AI and physicians for better patient outcomes. The challenge is learning when, where, and how to use AI responsibly while keeping clinical expertise at the center of care.
When AI Is a Powerful Diagnostic Ally
As AI tools become more accessible, both patients and clinicians are beginning to use them in diagnostic conversations — often in very different ways.
How Patients Are Already Using AI
Physicians have long had to contend with the "expertise" of patients, based on little more than internet searches. Artificial intelligence (AI) tools have only accelerated that trend. Large language models (LLMs) now generate seemingly personalized diagnostic suggestions that sound clinical and confident — without the wait or the copay.
Whether you're treating a walk-in patient with a self-diagnosis from ChatGPT or reviewing medical AI output in a more complex case, the question isn’t “Does AI have value?” It’s “How can I use AI well?”
This issue is more complex than the ‘AI diagnosis vs. doctors’ framing suggests. While several high-profile studies suggest that LLMs can match or outperform physicians on narrow diagnostic tasks — including ophthalmology screening accuracy (JAMA Ophthalmology, 2024), cardiovascular disease prediction (JAMA Cardiology, 2021), and dermatologic diagnosis (JAMA Network Open, 2024) — clinical decision-making is rarely that straightforward.
How Clinicians Use AI to Support Diagnosis
Physicians are also using AI. Many are exploring how the technology can support their diagnostic process by cross-checking differentials, reviewing scans, summarizing notes, or generating documentation. Health care systems are integrating AI into triage procedures, using machine learning for medical diagnosis support, and streamlining radiology workflows.
AI models have already matched or exceeded human accuracy in some diagnostic tasks, such as breast cancer screening or diabetic retinopathy. But the future of AI in medicine isn’t guaranteed or solely about technology.
“At the end of the day, we have to focus on what improves patient care, and I think that's going to lead, in the next 10 to 15 years, to some pretty strange ways of humans and AI collaborating,” Dr. Rodman says. “There may be some situations where you'll want the AI making the decision, and there are probably going to be some situations where you just want the human — where the AI actually decreases performance for the patient."
That complexity is what makes AI systems both exciting and difficult to integrate, Dr. Rodman adds. There’s no single answer or handoff model that works across all specialties. But when AI works, it has the potential to reduce diagnostic error, improve efficiency, and expand access to high-quality care.
Why Human Judgment Matters More Than Ever
AI may be impressive, but it doesn’t understand patients — and it can’t think like a clinician.
Understanding Clinical Context is Critical
AI models can replicate certain types of clinical reasoning, but only when the logic is clearly defined and easy to trace. That’s rarely the case in real-world practice, where diagnostic and personalized treatment plans often depend on patient context.
"We make a lot of management decisions under considerable uncertainty, and those decisions actually take a lot of patient context, take a lot to understand,” Dr. Rodman explains. “It's not just reading the guidelines, it's understanding who the patient is, the context of your health system, what their goals are, and then taking the evidence and applying it to that language model."
This kind of reasoning can be difficult to capture in training data, especially when it’s not written down. Even when documented, the reasoning might not be labeled in ways a model can interpret. That makes it difficult for AI to learn how experienced clinicians actually think. The results may look polished, but they reflect only a partial picture of the decision-making process.
"All of that makes it really challenging — not impossible, but really challenging — for language models to do a lot of the more nuanced medical decision making, even though they can do really impressive diagnostics," Dr. Rodman says.
Bias and Blind Spots Still Matter
Even when AI offers plausible suggestions, it doesn’t always improve care. One risk is confirmation bias, where instead of correcting a diagnostic error, the model might reinforce it. "The problem with these algorithms is they have a tendency to agree with you. If you're wrong and you use one of these things, it might just end up confirming you being wrong," Rodman notes.
Using artificial intelligence in healthcare doesn’t guarantee better performance, especially when clinical judgment is already compromised.
How AI Is Already Supporting Clinical Reasoning
Existing AI in medical diagnosis examples span disciplines and specialities. Radiology platforms use AI tools to scan for abnormalities. Primary care teams use symptom checkers to organize patient-reported complaints. Oncologists turn to predictive models for another layer of insight as they weigh treatment paths. But not every use happens behind the scenes.
Dr. Rodman describes a case that unfolded bedside. A patient presented with abnormal labs, but diagnostic options were limited because she was pregnant. After exhausting standard routes, he sat with the patient and opened ChatGPT.
“The two of us together talked to the language model … and then I told it what I knew, the patient filled in some of the things that she knew, and then at the end of it, it spat out a list of things that we could be missing,” Dr. Rodman says.
Notably, ChatGPT didn't provide knowledge Dr. Rodman didn’t already have, but it still improved the patient encounter:
"The list was basically the same as what I had come up with on my own. However, the patient felt so much more confident … because we had worked together with an AI to make sure we weren't missing anything,” he says. Adding AI to the physician-patient dynamic helped “reinforce the therapeutic relationship."
How to Build a Collaborative Model Between AI and Clinicians
To make AI effective, it must work seamlessly within clinical workflows — and clinicians must be trained to use it with confidence and oversight.
Embed AI in the Clinical Workflow
Many physicians are already using AI in healthcare, even if they don’t realize it. These tools are increasingly embedded into clinical practice to flag abnormalities in imaging, gather relevant guidelines, and suggest next steps. When medical professionals use these features as part of their electronic health records (EHR) platforms and triage software, they feel like part of the workflow.
While such integration makes AI-powered medical diagnosis easier to use, it also makes it harder to question its recommendations. AI algorithms that generate recommendations without a clear rationale don’t just waste time; they risk patient safety. Clinicians need to know what kind of data the system used, how the output was generated, and whether the model is drawing from relevant patterns. Otherwise, the output can’t be trusted or corrected.
Products like DynaMed’s point-of-care AI summaries, and Epic’s ambient note capture systems are already shaping how physicians interact with clinical data. But many physicians aren’t trained to assess these systems’ behind-the-scenes actions. That's why training matters.
Learn, Train, Test, Repeat
Clinicians need education to understand LLM behaviors, where they go wrong, and what tasks they’re suited to. That education requires hands-on experience with how the AI behaves in common diagnostic tasks. Case-based CME modules, internal pilot programs, and clinical decision support walkthroughs are all essential for helping clinicians understand how to use AI.
Dr. Rodman notes how some of the most promising uses for AI and automation are also the least supported. Chronic condition management — hypertension, diabetes, other guideline-based care — is a natural fit for automation. But such tools remain underused, largely because regulatory frameworks haven’t kept pace with development.
Addressing these gaps requirespolicy shifts. Institutions that deploy AI will also need to prioritize oversight, including documentation standards, model validation protocols, and clear escalation pathways. No model should be deployed without a mechanism for accountability. After all, AI doesn’t make decisions in a vacuum. Someone has to be responsible for AI outputs and actions.
The Future of Diagnosis Is Team-Based: Doctor + AI
The future of healthcare isn’t a zero-sum game of AI vs. doctor diagnosis. Instead, humans and technology will work ever more closely. AI can increasingly handle pattern recognition at scale, surface overlooked possibilities, or prepare the ground for clinical reasoning. What it can’t do is make decisions with consequences. That remains a clinical task.
Dr. Rodman sees a path forward in workflows that use AI to support what physicians already do well. AI can be valuable for creating structured patient histories before a visit, summarizing relevant social or exposure data, or assisting with routine guideline application to make follow-up visits more focused and useful. But he adds that any AI assistant needs to leave room for human judgment.
The job isn’t to make diagnosis faster. It’s to make it better. That means balancing the advantages of automation with the responsibilities of care. Healthcare systems that succeed will be those that give their clinicians more to work with, not less to do.
As AI changes the clinical landscape, staying current means understanding more than just the tech — it means knowing how to integrate it into practice. Oakstone’s expert-led CME helps you stay informed, efficient, and prepared to lead. Explore our AI-focused CME offerings to stay ahead.