Which AI Should a Clinician Use for Medical Questions?

The question I get from clinicians is usually not theoretical anymore. It is not "will AI matter in medicine?" or "should doctors pay attention to this?" Most people who are asking me about these tools have already tried ChatGPT, or they have seen a colleague use OpenEvidence, or they have heard someone mention Doximity Ask, Mednet, Consensus, or Paperpile. The question is much more practical:

Which one should I use when I actually need a medical answer?

That sounds simple, but it is where a lot of confusion starts. These tools all sit behind a similar interface. You type a question, you get an answer, and the answer may come with citations, links, or a table. From the outside they can feel interchangeable. In practice, they are very different products. Some are general reasoning and writing tools with better clinical wrappers. Some are evidence engines. Some are closer to literature search tools. Some are better thought of as ways to interrogate papers you already have.

So I would not start by asking which one is "best." That is too vague to be useful. I would start with a more clinical question: what kind of work am I trying to do, and what source layer do I need underneath the answer?

If I am writing patient-friendly language, drafting an appeal, organizing my thoughts before a talk, or turning a rough paragraph into something readable, a general LLM may be more than enough. If I am asking what a trial showed, what a guideline says, whether a drug is supported in a specific setting, or how experts handle a gray-zone scenario, then fluency is not enough. I need to know what the tool searched, what it actually read, and how easily I can get back to the source.

That is the frame for this comparison. Not "which chatbot is smartest?" but "which tool is appropriate for this kind of medical question?"

A Practical Comparison

Here is the table I wish someone had given me when these tools started multiplying. It is not meant to crown a winner. It is meant to help you decide where to start.

The important column is not the model name. It is the source layer. A clinician does not just need an answer that sounds reasonable. We need to know whether the answer is grounded in full text, abstracts, guidelines, drug data, a publisher relationship, uploaded PDFs, physician expert discussion, or a general web search. Those differences matter because they determine what the system can see and, just as importantly, what it can miss.

If I have a specific clinical evidence question, I am more likely to start with OpenEvidence, Doximity Ask, Mednet AI, or ChatGPT for Clinicians. If I am trying to map a field or build a paper table, Consensus is often a better first stop. If I already know which papers I care about and want to work through them systematically, Paperpile Ask AI, NotebookLM, Claude, or ChatGPT with the PDFs attached may be the better workflow. If I am drafting patient education language, explaining a concept, or organizing a paragraph, a general LLM is often fine.

The mistake is treating all of those tasks as the same task.

The Source Layer Matters More Than the Chat Box

Most clinicians are trained to be skeptical of unsourced claims, but AI makes that harder because the answer arrives already formatted, confident, and often very readable. A citation helps, but it does not solve the whole problem. A citation tells you the answer has an anchor. It does not prove the model interpreted the source correctly, weighted the evidence correctly, or noticed the detail that would change how you apply it.

This is where clinical judgment still matters. A tool can retrieve two real trials and summarize both without telling you why one should move practice more than the other. One trial may have a cleaner endpoint. Another may have better treatment completion. One may fit your patient population. Another may be technically positive but clinically less persuasive. That kind of weighting is often where expertise lives.

So when I look at a clinical AI tool, I am not only asking whether it gives citations. I am asking:

• Can I open the source quickly?
• Does it distinguish guidelines from primary trials?
• Does it tell me when evidence is thin?
• Does it separate what is known from what is uncertain?
• Does it make it easy to notice when an answer is based on abstracts rather than full text?
• Does it help me inspect the evidence, or does it encourage me to stop at the summary?

The best tools are not the ones that make me feel done. They are the ones that get me to the right next source faster.

How I Would Actually Use Them

I would use ChatGPT for Clinicians when the work is mixed. That is probably its strength. Some clinical tasks are not pure evidence retrieval. They are part question, part synthesis, part writing, part formatting, and part workflow. A prior authorization letter, a patient explanation, a guideline comparison, a structured memo, or a reusable trial-review prompt all fit that pattern.

The clinician-specific wrapper matters because access is verified and the product is being shaped around medical use cases. But the reason I would use it is broader than that. It is a workspace. If I have a good way of reviewing trials, I can turn that into a reusable instruction: every time I ask about a trial, extract the population, intervention, comparator, endpoint, follow-up, completion rate, limitations, and what would be misleading if I only read the abstract. That is much better than starting from a blank chat every time.

I would use OpenEvidence when I have a known clinical question and want to get oriented quickly. This is the tool that most feels like a clinical evidence engine. It is built around medical literature grounding, citations, verified healthcare professional access, and a growing set of workflow features such as calculators, drug information, guideline-style modules, patient handouts, administrative support, and tables.

That combination is useful because many clinical questions are not open-ended research projects. Sometimes you just need to know what the evidence says about a specific issue. What did a trial show? What are the main data behind a recommendation? What is the evidence for this drug in this setting? OpenEvidence is good at getting you to that starting point quickly. The caution is also obvious: when a tool is fast and polished, it can make the evidence feel more settled than it really is. I still click through when the answer matters.

I would use Doximity Ask when I am already in a clinician workflow and want the answer to turn into something practical. Doximity's advantage is not just the Q&A box. It is the surrounding ecosystem: Ask, Scribe, Dialer, messaging, fax, uploaded documents, patient education, and administrative drafts. That makes it feel less like a standalone research tool and more like a layer inside the day-to-day work of a clinician.

That may sound mundane, but mundane is where a lot of AI value lives. The useful clinical tool is not always the one with the longest literature review. Sometimes it is the one that helps you turn a question into a patient explanation, a note, a letter, or a follow-up message without leaving the workflow.

I would use Mednet AI when the question is a gray-zone practice question. Medicine is full of situations where the trial exists, the guideline exists, and the patient still does not fit neatly. In those cases, expert practice can be informative. It is not the same thing as randomized evidence, and it should not be treated as such, but it is still useful to know how experienced physicians think when the guideline stops short.

That is the conceptual value of Mednet. It sits closer to expert community knowledge than a pure literature search engine. I would not use it to replace primary-source review, but I would use it to understand how clinicians are reasoning around uncertainty.

I would use Consensus when I am trying to understand the literature landscape rather than answer one bedside question. It is useful for finding trials, comparing papers, identifying systematic reviews, building study tables, and getting oriented in an unfamiliar evidence base. That makes it more research assistant than clinical colleague, which is not a criticism. It is a different job.

I would use Paperpile Ask AI when I already have the source set. This is an underappreciated distinction. Sometimes the best answer is not on the open web, and it is not in a generic search result. It is in the 20 PDFs you already collected because you know those are the papers that matter. Paperpile's value is that it connects that library to the AI assistant you already use.

That is particularly useful for structured reading. If I want to work through a stack of papers, I want the model looking at the actual PDFs and extracting the same fields each time: eligibility, intervention, comparator, endpoint, follow-up, results, completion rate, limitations, and what would be misleading from the abstract alone. In that setting, the model is not pretending to know the field. It is helping me read the sources I selected.

And I would still use general LLMs constantly, just not for everything. ChatGPT, Claude, Gemini, and Perplexity are excellent for explanation, rewriting, brainstorming, patient-friendly language, talk outlines, coding help, and turning a messy thought into a clearer one. The problem is when a free general chatbot gets asked to be a medical librarian, guideline engine, drug database, expert panel, and primary-source reader all at once.

That is too much to ask from the wrong tool.

Known Unknowns Are the Sweet Spot

These 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. An even better question is: what did CC001 show, what was the intervention, how completely was it delivered, what endpoint mattered, and what would an expert worry about before applying it to the 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 sources and miss the clinical frame.

The hardest question is still "what am I missing?" That is also one of the most important questions in medicine.

So I try to use AI in a way that makes me more curious, not less. If a tool gives me a clean answer, I ask what would change the answer. I ask what evidence is missing. I ask what an expert would worry about. I ask what conclusion would be misleading if I only read the abstract. Then I open the sources that matter.

That is not being anti-AI. That is using the tool without becoming passive.

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 different tools behave.

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

That is the question that separates a summary from a useful clinical reading aid. 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 and try to force every question through it. I would build a small stack and learn when to switch.

For quick clinical evidence questions, 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 papers I already collected, I would use Paperpile Ask AI, NotebookLM, Claude, or ChatGPT with the actual PDFs attached.

For everything else, a general LLM is still useful. I just would not ask a free general chatbot to be my evidence engine, librarian, clinical colleague, and 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?"

Sources Checked

• ChatGPT for Clinicians help page
• OpenAI's clinician announcement
• OpenEvidence homepage
• OpenEvidence 2.0 workflow update
• Doximity AI page
• Doximity GPT FAQ
• Doximity Clinical AI Suite announcement
• Mednet AI guidelines for physicians
• Mednet physician community overview
• Consensus research database
• Consensus Medical Mode
• Paperpile Ask AI help page
• Paperpile PDF-to-AI integration announcement