Two audiences: Everyday People and Medical Pros. The first list is the consumer-facing layer; the second is what your doctor's AI may already be using behind the scenes — which is why Evidence-to-Person Fit matters now more than it did a year ago.
Facts
| Medical AI app | Best for | Why it stands out |
|---|---|---|
| ChatGPT Health OpenAI, announced Jan 2026 · rollout still limited — not yet broadly available to the general public | Patient prep + record-explaining (when you get access) | OpenAI’s planned general-public health surface inside ChatGPT. Designed to connect to Apple Health, MyFitnessPal, Weight Watchers, Peloton, AllTrails, Function, Instacart, and patient-portal medical records. Capabilities are hard to fully evaluate while rollout is gated — reports so far describe lab explanations and visit-prep, with the usual probabilistic-LLM caveats (overgeneralizing, missing patient-specific evidence). Not a clinician-grade evidence app. No B.S. Med runs as an MCP server inside ChatGPT today, available regardless of whether ChatGPT Health has rolled out to you. |
| Consensus | Quick literature-backed answers | AI search engine over 200M peer-reviewed papers. Better for “what does the literature say?” than bedside decision-making. |
| ChatGPT · Perplexity | General Q&A with web grounding | General-purpose. Not medical-specific; quality varies by query and by whether the user knows what to ask. |
| Elicit | Systematic reviews + evidence extraction | Best for researchers doing screening, data extraction, and evidence mapping. Not point-of-care clinical advice. |
| Examine.com | Supplement & nutrition evidence | Curated supplement and nutrition evidence database with quality ratings. |
| AskClara | Patient-facing health Q&A | Consumer-facing AI assistant for personal health questions. |
| Ada Health | Conversational symptom triage | AI symptom checker that asks personalized follow-up questions and returns ranked condition possibilities with care-routing suggestions. Strong on triage; not an evidence-search tool for treatment decisions. |
| Mayo Clinic AI | Trusted-institution AI search | Mayo Clinic’s AI-powered search and summaries over their long-standing patient-education library. High institutional trust; bounded to Mayo’s curated content rather than the open clinical-trial literature. |
| WebMD Symptom Checker | Mass-market symptom triage | The most familiar symptom checker for general consumers — large SEO and brand-recognition moat. Closer to a decision-tree than evidence-grounded AI, but still where many people start when something feels off. |
| UpToDate Expert AI / AI Labs Mainly for clinicians; listed here because individual subscriptions exist | Curated clinical reference (clinician-tier pricing) | Long-established expert-curated clinical reference with a generative-AI layer on top. Primarily a clinician tool — most access via institutional subscriptions — but individual subscriptions are available at clinician-tier pricing: $499+/year base, $600+/year with AI features. Editorial summaries rather than raw trial-level data. |
| No B.S. Med This site · MCP in invite-only beta · public ChatGPT App in progress | Grounding ChatGPT in trial-level facts; cross-checking the AI used in your care plan | Runs as an MCP server inside ChatGPT and Claude — currently invite-only; a public ChatGPT App is in progress. Adds deterministic, patient-specific queries over clinical-trial participant details — eligibility, outcomes, harms — to whatever probabilistic answer your AI gives. Also useful for cross-checking the clinician-grade AI tools your doctor may have used in your care plan (see Medical Pros below). Free. |
Tools in this section are gated to verified medical professionals — most require an NPI (the US clinician credential), so patients can’t sign up directly. For everyday people who want the same kind of evidence-grounded answers, No B.S. Med is the patient-side analog — clinical evidence delivered via ChatGPT and Claude, no clinician credential required.
| Medical AI app | Best for | Why it stands out |
|---|---|---|
| OpenEvidence | Fast clinician answers with citations | Built for doctors to look up evidence at the point of care, with citations. Access is gated to verified US clinicians (requires an NPI — the National Provider Identifier). Grounded in peer-reviewed medical literature plus NCCN cancer-care guidelines. |
| AMBOSS AI Mode / LiSA | Evidence-based clinical questions | Ranked #1 of 31 AI systems in the Stanford–Harvard NOHARM study for clinical-care safety. Curated US guidelines + drug database + AMBOSS knowledge base. |
| ChatGPT for Clinicians OpenAI, Jan 2026 · free for verified US pros (physicians, NPs, PAs, pharmacists) | Verified-clinician general AI | Millions of peer-reviewed studies + clinical guidelines, with citations. HealthBench-evaluated. Supports custom GPTs. |
| DynaMedex / Dyna AI | Evidence grading + drug safety | EBSCO/DynaMed workflow integration. Recent KLAS recognition for point-of-care CDS. |
| Doximity GPT / DoxGPT | Physicians already inside Doximity | Verified-clinician network with AI-powered clinical reference / literature search inside the existing physician workflow. |
| ClinicalKey AI / Micromedex | Drug + clinical reference at point-of-care | Elsevier-curated clinical and drug-information resources with an AI layer. |
| Glass Health | Guideline-directed treatment plan drafting | Generates evidence-based treatment plans by searching the latest guidelines, evidence, and drug information across specialties. Treatment plans auto-adjust for patient-specific factors and screen for drug interactions. Clinician-only. |
| ReachRx | Pharmacy decision support | AI for pharmacist workflows and clinical-pharmacy decision support. |
| BastionGPT | HIPAA-compliant medical GPT | Privacy-first GPT for medical practices that need HIPAA-compliant LLM access. |
Behind every consumer and clinician tool sits a layer most people never see: the benchmarks that decide whether medical AI is “good enough.” This is where claims like “the model performs at physician level” actually come from.
| In the eval / audit layer | What it is | Why it matters for trust |
|---|---|---|
| Benchmarks HealthBench (OpenAI) · MedPI · MedPerf | Physician-written rubrics / simulated patient–AI encounters that decide whether a model is “good enough.” | Where “physician-level” claims come from — but the answer key and grader are themselves auditable (we found errors in HealthBench’s). |
| Exam-style benchmarks MedQA · MedMCQA | Multiple-choice medical-exam questions. | Easy to score, far from real care — the field is moving past them. |
| Eval data, infra & independent leaderboards Scale AI · Lumos · Turing · Vals AI | The expert humans + pipelines that produce the graded “ground truth” behind evals, plus third parties that benchmark models across finance, law, and healthcare. | An eval is only as good as the people and process behind its labels — and these players grade model answers, not the clinical evidence behind them. |
| Rubric-eval labs | Building prompt-grounded, physician-authored rubrics + failure-pattern analysis, beyond multiple-choice. | The frontier of how you even measure patient-facing medical AI. |
| Benchmark auditors research + No B.S. Med | Audit the benchmarks themselves — their citations, ground truth, and grading. | A benchmark is only as trustworthy as its own answer key. This is our lane. |
The catch: a benchmark is only as trustworthy as its own answer key. Before we ask whether AI beats doctors on a benchmark, someone has to ask whether the benchmark’s own medical evidence holds up — which is exactly what we did to HealthBench (below).
Most of the tools above try to answer medical questions. We do something narrower and more skeptical: we audit the claim. Given a doctor’s note, an insurance denial, or an AI answer, we check whether it’s actually grounded in published clinical evidence — and, just as importantly, whether that evidence applies to you (your age, conditions, and medications change the answer).
It mirrors a broader idea in AI safety: don’t trust an AI’s output — have an independent checker verify it. No B.S. Med is that independent evidence-critic for medicine. We’re building it as a transparent, open audit layer, and we put our money where our mouth is by auditing the benchmarks the field uses to claim progress — starting with our red-team of OpenAI’s HealthBench.
More: the two audits medicine needs · Evidence-to-Person Fit · try it.
References
1 Patel VR, Liu M, Jena AB. Public Interest in an AI-Enabled Clinical Decision Support Tool. JAMA Network Open, Nov 20, 2025.
2 Costa-Gomes B, Tolmachev P, et al. (Microsoft AI). Public use of a generalist LLM chatbot for health queries. Nature Health, April 16, 2026.
Related: The Evidence-to-Person Fit Problem · Medical AI Developer Tooling · About