Which AI Should a Clinician Use for Medical Questions?

I just came back from a trip where I gave a talk on LLMs and AI to healthcare professionals as part of an Innovations in Cancer 2026 program. I expected curiosity. I did not expect the level of energy in the room.

The questions were not "how do we stop this?" They were "how do I start?" "What can I use safely?" "How do I know when to trust it?" "How do I teach my fellows?" "What should my clinic be doing now before the institution gives us a policy two years late?"

That is why I keep finding the standard AI-in-healthcare headline a little stale. You know the one: physicians are apprehensive, worried, skeptical, resistant. There is truth in it, but it is not the whole truth, and at this point it may not even be the most useful truth.

The AMA's 2026 physician survey found that more than four in five physicians now use AI professionally, more than double the 2023 rate. It also found real concern: privacy, validation, skill loss, liability, and the patient-physician relationship. That is not contradiction. That is what serious adoption looks like. The doctors are not anti-AI. They are asking the questions anyone responsible for another human being should ask before putting a probabilistic system into the workflow.

This piece is for the clinician who has moved past the abstract question of whether AI belongs in medicine and is now asking a more practical question: which tool should I use when I need a medical answer?

The tempting question is "which one is smartest?" Is ChatGPT better than OpenEvidence? Is Doximity Ask better than Consensus? Is Mednet AI more useful because it has expert answers? Should I just use Claude or Gemini because I already pay for them? I understand the instinct, but I think it is the wrong frame. These tools are not interchangeable brains sitting behind different logos. They are different ways of getting to evidence, getting work done, and deciding what still needs a human expert.

The better question is this: what did the tool search, what did it actually read, and how easy is it for me to verify the answer?

That is the question that matters in clinic. If I ask, "What did CC001 show?" I do not want a confident paragraph that sounds like a board review book. I want the trial, the population, the intervention, the endpoint, the result, the limitation, and a path back to the paper. If I ask whether two trials showed the same thing, I do not just want the headline result. I want to know whether one trial had better completion of the intervention, cleaner follow-up, a more relevant population, or a design detail that would make an expert weight it differently. Free general ChatGPT can help explain and draft, but clinical evidence work is not just language work. It is source work.

The core rule

Use general LLMs for thinking, drafting, and reframing. Use clinical evidence tools when the answer depends on what a paper, guideline, label, trial, or expert community actually says.

The Comparison Table

Here is the practical version. I am not trying to crown one winner, because the right answer depends on the job. I am trying to make the first decision easier: where should you start, what kind of source layer is underneath the answer, and where can the tool still flatten the evidence?

Tool	Access	Best use	Source layer and vetting	Personalization and workflow	Main limitation
ChatGPT for Clinicians	Free for verified US clinicians with an NPI and license check	Mixed clinical work: evidence questions, explanation, drafting, documentation, and reusable prompts	Uses clinical search and cited sources; clinician access is verified, but the synthesis still needs source-level review	Strongest general workspace. A good trial-review prompt can become a reusable skill or saved instruction for extracting endpoints, completion rates, limitations, and what would mislead you from the abstract alone	Broad enough that it can sound right while missing source weighting or clinical nuance
OpenEvidence	Free for verified US healthcare professionals	Fast evidence lookup for known clinical questions	Medical literature and evidence modules; citations anchor the answer, but you still need to click through	Increasingly platform-like: evidence answers, calculators, drug information, guideline-style modules, patient handouts, administrative workflows, and tables	Speed can make uncertainty look smaller than it is
Doximity Ask	Free for verified Doximity clinicians	Clinical Q&A that turns into workflow output	Literature, drug data, uploaded documents, and Doximity workflow context; Doximity describes clinician workflows as HIPAA-compliant	Strong if you already use Doximity: Ask, Scribe, Dialer, messaging, fax, patient education, and administrative drafts sit near each other	Best inside the Doximity ecosystem; less natural as a standalone research desk
Mednet AI	Free registration on TheMednet	Gray-zone questions where expert practice matters	Expert Q&A, physician discussions, cited evidence, and community practice patterns; the vetting is clinician judgment plus source links	Personalized to specialties and physician community context, especially when guidelines stop short	Expert practice helps, but it is not the same thing as trial-level evidence
Consensus	Free tier plus paid features	Literature mapping, paper comparison, and research orientation	Broad research database, Medical Mode, guidelines, full text when available, and abstracts or metadata when full text is unavailable	Good for building paper tables, finding systematic reviews, and orienting yourself to a literature base	Not clinician-specific; you still need to decide which papers matter clinically
Paperpile Ask AI	Paperpile account plus the AI assistant you choose	Reading your own PDFs at scale	Your selected PDFs, sent to ChatGPT, Claude, Gemini, Copilot, or NotebookLM; the source set is only as good as your library	Strong personalization because the workflow starts from your own papers and prebuilt prompts	It helps you read papers; it does not decide whether you picked the right papers
Free general LLMs	Usually free or low cost; no clinician verification	Explaining concepts, drafting, brainstorming, simplifying	Model training data, memory, uploads, and web search if enabled; usually no consistent clinical vetting	Flexible if you use custom instructions, projects, memory, or uploaded files	Highest risk of shallow synthesis, hallucinated citations, stale information, and false confidence

This table is intentionally practical. It does not ask which model has the highest benchmark score. It asks what a clinician actually needs to know before using the tool: how do I get access, what can I personalize, what workflow does it sit inside, what source layer is underneath it, and where can it still miss nuance?

If I have a known clinical question, I would usually start with OpenEvidence, Doximity Ask, Mednet AI, or ChatGPT for Clinicians. If I am trying to map a literature area, I would reach for Consensus. If I already have the papers and want to read them at scale, I would use Paperpile Ask AI, NotebookLM, Claude, or ChatGPT with the PDFs attached. If I am trying to explain a concept, write patient-facing language, draft a letter, or think through how to present something, a general LLM is often enough.

But if the answer changes care, I still want the primary source.

The Difference Is Not the Chatbot. It Is the Source Layer.

Most of these tools look similar from the outside. You type a question, a box gives you an answer, and the answer may have citations or links. The interface makes them feel more similar than they are, which is exactly why clinicians need to understand the source layer underneath the answer.

A tool may search the open web, PubMed abstracts, full-text PDFs, society guidelines, drug databases, publisher-licensed material, your private library, or a database of physician expert answers. Those are not small differences. They determine what the model can see, and they also determine what it can miss. If a tool only sees abstracts, it may miss the detail buried in the methods or supplement. If it has full text from some publishers but not others, it may be deeper in one part of the literature than another. If it is built around expert Q&A, it may surface practice wisdom that never appears in a guideline, but it may also reflect habit, local culture, or the opinions of a small expert group.

That is why citations are necessary but not sufficient. A citation tells you the answer has an anchor. It does not tell you the model interpreted the paper correctly, weighted the evidence correctly, or noticed the trial-design issue an expert would care about.

This is the part that beginners often miss. They assume hallucination means "the model made up a paper." That happens, but it is the crude version of the problem. The more subtle version is when every citation is real, every sentence sounds reasonable, and the overall interpretation is still too flat. Clinical expertise often lives in the weighting. Two trials can point in the same direction, but one may matter more because the intervention was actually completed, the endpoint was cleaner, the population was closer to your patient, or the follow-up was long enough to believe the result. A model can retrieve both papers and summarize both correctly while still failing to tell you which one should move your practice.

How I Would Use Each Tool

ChatGPT for Clinicians is the broadest of the group. OpenAI describes it as a clinician-specific workspace with trusted clinical search, citations, deep research, pre-built skills, starter prompts, documentation support, CME support for eligible clinical questions, and an individual BAA option for eligible accounts. That makes it useful when the task is mixed: part evidence review, part reasoning, part writing, part workflow.

That breadth is the advantage. It is also the risk. The more a tool can do, the easier it is to let it do too much. I would use ChatGPT for Clinicians to draft a prior authorization letter, compare guideline language, generate a patient explanation, or produce a cited research memo. I would not let the memo be the last step before a clinical decision. The right move is to make it show sources, then read the sources that matter.

The more interesting use case is reusable clinical reading. If you have a prompt that works, turn it into a skill file or saved instruction: every time I ask about a trial, extract the population, intervention, comparator, endpoint, main result, completion rate, follow-up, limitations, and what would be misleading if I only read the abstract. That is where a general clinician workspace becomes more useful than a one-off chat. It remembers the way you want evidence handled.

OpenEvidence feels more like a clinical evidence engine. It is built for the moment when you have a specific medical question and want to get oriented quickly. Its public materials emphasize medical literature grounding, verified healthcare professional access, NEJM and JAMA content relationships, and newer workflow features like calculators, drug monographs, guideline modules, patient handouts, administrative support, and tables.

This is probably where I would start for many "known unknown" questions. What did CC001 show? What are the main trials behind this recommendation? What does the evidence say about this drug in this setting? The tool is useful because it gets you to the evidence quickly. The danger is that speed can trick you into stopping too early.

Doximity Ask, formerly DoxGPT, is different because it lives inside a physician workflow platform. Doximity describes it as free for verified clinicians, HIPAA-compliant, able to handle PHI under its protocols, and grounded in guidelines, systematic reviews, peer-reviewed literature, drug data, and full PDFs. It also connects to Doximity's broader Clinical AI Suite: Ask, Scribe, Dialer, messaging, fax, and other productivity tools.

That means its best use case may not be "sit down and perform a literature review." It is more practical than that. You are already in a clinical workflow, you need an answer, a patient explanation, a note template, a prior authorization letter, a scribe-supported note, or a quick synthesis from an uploaded document. For that kind of work, integration matters. The tool is not just answering a question. It is helping turn the answer into something you can use.

Mednet AI has a different center of gravity. Its public materials emphasize evidence plus physician expertise: peer-reviewed literature, clinical trials, guidelines, reviews, meta-analyses, expert Q&A, physician discussions, and polls. That makes it valuable for questions where the literature exists but does not fully answer the clinical situation.

That is common in medicine. Guidelines are useful, but they are not the patient in front of you. Trials are useful, but the inclusion criteria rarely match perfectly. Expert practice is not evidence in the same way a randomized trial is evidence, but it is still information. When I want to know what experienced physicians actually do in the gray zone, Mednet AI is conceptually different from a pure literature engine.

Consensus is not a clinician-only tool. It is a research search and synthesis tool with a large academic database, Medical Mode, guideline coverage, full-text access when available, publisher partnerships, and uploaded PDF functionality. That makes it useful when the task is not "give me a quick clinical answer" but "help me understand the literature landscape."

I would use Consensus to find trials, compare papers, build a study table, identify systematic reviews, or get oriented to an unfamiliar evidence base. It is closer to a research assistant than a clinical colleague. That is not a criticism. It is a category distinction.

Paperpile Ask AI is not really an answer engine at all. It is a bridge from your PDF library to the AI assistant you already use. Paperpile can send selected PDFs to ChatGPT, Claude, Gemini, Copilot, or NotebookLM, optionally with structured prompts. That sounds modest, but it solves a real problem: most of the time, the best source set is not the whole internet. It is the pile of papers you already know you need to read.

This is where Paperpile becomes useful at scale. If I have 20 papers and want a structured pass through each one, I do not want a generic web answer. I want the model staring at the actual PDFs. I want a table of eligibility, intervention, comparator, endpoint, follow-up, result, completion rate, and limitation. I want the model to help me read, not pretend it has already understood the field.

The funny prompt, like asking for an Onion-style article about a paper, is not the point. The point is that once the model has the paper, you can interrogate it in different ways. Serious ways first. Weird ways later.

General LLMs still matter. ChatGPT, Claude, Gemini, and Perplexity are part of the daily stack for many clinicians and researchers. I use them for explanation, drafting, rewriting, brainstorming, patient-friendly language, workflow design, and thinking through how to frame a problem. The mistake is asking them to be every tool at once.

If the question is "help me explain hippocampal avoidance to a patient," a general LLM can be excellent. If the question is "what does the evidence show, and which trial should I trust more," I want a grounded tool and then the primary source.

Known Unknowns Are Where These Tools Shine

Clinical AI tools are strongest when you know the shape of the question. "What did CC001 show?" is a good AI question because there is a specific trial, a specific answer, and a source you can open. A stronger version is: what did CC001 show, what was the intervention, what was actually completed, and what would an expert worry about before applying it to a patient in front of me?

"What should I know about whole-brain radiation and cognition?" is harder. Now the tool has to decide whether to retrieve memantine, hippocampal avoidance, NRG CC001, RTOG 0614, QUARTZ, prognosis papers, supportive care literature, or guidelines. It may do that well, but it may also retrieve the obvious papers and miss the clinical frame. "What am I missing?" is the hardest question, and it is also one of the most important questions in medicine.

That is where AI should make you more curious, not more complacent. If the tool gives you a clean answer, ask it what would change the answer. Ask what evidence it did not find. Ask what an expert would worry about. Ask which conclusion would be misleading if you only read the abstract. Then open the source. This is not anti-AI. It is how you use these tools without becoming passive. Every tool in this table can still miss nuance. The better tools make the source trail easier to inspect; they do not replace the inspection.

A Prompt I Would Actually Use

Use this in ChatGPT for Clinicians, OpenEvidence, Doximity Ask, Mednet AI, Consensus, or a PDF-grounded workflow. If you use ChatGPT for Clinicians, turn it into a reusable skill or saved instruction so every trial gets read with the same structure. Then compare how the tools behave.

Evidence Tool Stress Test

I am a clinician trying to understand the evidence behind: [clinical question].

First, identify the highest-yield primary sources, guidelines, or trials.

Then make a table with:

source name
year
population
intervention or exposure
comparator
main endpoint
main result
important limitations
why an expert might weight this source more or less heavily

Do not just summarize the conclusion. Tell me what could be misleading if I only read the abstract.

If evidence is mixed, separate what is established from what remains uncertain.

The most important line is this: tell me what could be misleading if I only read the abstract.

That is the question that separates a summary tool from a thinking tool. It forces the system to look for the places where evidence gets flattened. In medicine, those flattened places are often where the real judgment lives.

My Current Recommendation

For a beginner clinician, I would not pick one tool. I would build a small stack and learn when to switch.

For quick clinical evidence, I would start with OpenEvidence or Doximity Ask. For clinician-specific general work, I would use ChatGPT for Clinicians. For gray-zone practice questions, I would try Mednet AI. For literature mapping, I would use Consensus. For reading many PDFs, I would use Paperpile Ask AI, NotebookLM, Claude, or ChatGPT with the actual papers attached.

For everything else, a general LLM is still useful. Just do not ask a free general chatbot to be your evidence engine, your librarian, your clinical colleague, and your primary-source reader all at once.

The point is not to collect AI subscriptions. The point is to match the tool to the evidence problem.

A free chatbot is fine for "explain this to me." It is not where I would stop for "what does the evidence show?"