One patient. Multiple perspectives. A network of specialized AI agents continuously adds layers of insight to reveal the complete clinical picture.
See How It Works →Built for physicians
Evidence
Built on evidence that holds up in real clinical decisions—drawing from sources like New England Journal of Medicine, JAMA, National Comprehensive Cancer Network, and hundreds of peer-reviewed journals worldwide.
Available at no cost for verified physicians.
Workflow
Get to the right evidence in seconds—automatically tailored to the patient's condition, clinical context, and your practice patterns. No fragmented tabs. No chasing papers across PubMed.
The Problem
Between information and intelligence. Between data and decision. Between a CDS alert firing and a clinical insight that actually helps. That is where medical errors happen — and where we live.
Every tool you use today gives you pieces — your EHR gives you data, UpToDate gives you literature, scribes capture your words. But none of them actually help you think through a patient. That cognitive gap remains yours, every single time.
You still build the differential. You still synthesize the decision. The cognitive load hasn't changed — it's just moved.
They don't think clinically. They don't flag the troponin trend you might have missed at 3am.
They don't reason. Alert fatigue is real. When everything fires, nothing matters.
17–45% error rate in clinical contexts. You cannot trust it with a patient's life.
Between information and intelligence. Between data and decision.
That is where we live.
The Solution
A team of specialist agents that monitor, reason, and act — continuously. Like having a attending for every specialty who never sleeps, never misses a lab, and never forgets a guideline. They think before you ask.
Unlike AI chatbots that start fresh each time you open a window, our agents maintain persistent awareness of your patients. Think of it like having a dedicated resident who has reviewed everything about your patient and is always watching for changes.
You don't need to ask. When your patient's troponin starts trending up alongside tachycardia, the system notices. When a post-op patient's blood pressure slowly drops overnight while nursing notes say "stable," the system catches the trend no single vital sign would reveal.
Think of each agent as a specialist colleague you can silently consult — your cardiologist, your pharmacist, your intensivist. They work together on every case, share findings, and only surface a recommendation when they've reached consensus.
The Orchestrator is the "attending physician" of the agent team. It reads the clinical situation, decides which agents to activate, resolves conflicts when agents disagree, and determines when consensus is strong enough to surface a recommendation. It uses 65× less compute than single-model systems while handling up to 80 simultaneous clinical tasks.
The system never takes action on a patient without your approval. It provides intelligence, not authority. You are always the final decision maker — this is designed into the architecture, not just a policy.
Agents watch silently. Alert only for critical safety issues. You drive all decisions. Ideal for getting started.
Agents surface recommendations with full evidence links. One-click execution or dismiss. You approve every action. This is the default.
Routine low-risk actions (documentation, standard orders) execute automatically. High-risk actions always require your approval.
Doctorassist.AI sits as a layer between your data sources and your interface. It does not replace your EHR — it sits alongside it, reading data in and returning recommendations through a panel you already use.
Intelligence Features
Five systems working in concert — a living library of clinical protocols, a catalogue of known pitfalls, dual-speed reasoning, a second brain for every physician, and a knowledge graph connecting your patient to every relevant piece of evidence.
01 — Skill Repository
Every guideline, template, decision tree, and institutional protocol — version-controlled, specialty-tagged, and continuously updated. Every agent draws from the same authoritative source. When AHA updates a guideline, the system updates automatically.
Admission notes, discharge summaries, H&P structures — pre-loaded with evidence-based clinical reasoning scaffolding.
Branching logic for ACS workup, sepsis evaluation, PE risk stratification, and 200+ clinical pathways with embedded guideline citations.
Your hospital's formulary, antibiogram, preferred imaging protocols, and paging trees — encoded once, applied everywhere.
Optimized triggers built from real physician workflows. Invoke complex multi-agent reasoning with a single sentence.
02 — Continuous Learning
We continuously monitor wrong assumptions, missed patterns, and best practices from real clinical cases. Whenever you encounter a similar situation, the system instantly brings back those insights — helping you make more accurate, confident decisions every time.
Atypical presentation without chest pain occurs in 23% of diabetic patients. Previous systems over-weighted "no chest pain = low ACS risk."
Post-op patients can compensate hemodynamically until abrupt decompensation. Trend analysis over 4h outperforms single-point snapshots by 3.4×.
Contrast-induced nephropathy risk was routinely missed when eGFR wasn't surfaced alongside CT orders. Now cross-referenced automatically.
Culture results available at 72h but empirical broad-spectrum antibiotics continued. Proactive culture review reduces resistance risk and length of stay.
Physicians consistently underdocumented decision-making complexity, leaving billing on the table and misrepresenting clinical effort in records.
A baseline INR of 2.4 read as acceptable — but new amiodarone initiation predicted to push it to 3.8+ within 2 weeks. Static values without trajectory modeling cause preventable bleeds.
03 — Dual Process Reasoning
Based on how human experts think: fast intuitive pattern recognition for time-critical emergencies, and slow deliberate reasoning for complex clinical questions. The system knows which mode to use — and switches seamlessly between them.
"Something is wrong. Act now."
"Let's think through this carefully."
04 — Clinical Memory
From concept of a Personal Knowledge Management system — we've built the clinical equivalent. Every insight, pattern, and near-miss you encounter is captured automatically and returned to you at exactly the moment it becomes relevant again.
"The goal of building a second brain is not to accumulate more information — it's to offload the cognitive burden of remembering so you can focus on creating."— Building a Second Brain (applied here to clinical cognition)
How memory builds over time (Forte's CODE applied to clinical work)
Every case interaction, override, flag, and annotation captured automatically without adding to your workload.
Sorted into: Active Patients (Projects) → Specialty Domains (Areas) → Evidence Library (Resources) → Closed Cases (Archive).
A 4-hour shift distils to a 5-point handover. A week distils to 3 pattern insights. A month surfaces as specialty-level learnings in agent reasoning.
Your personal clinical memory resurfaces when patterns match: "You've seen this before — last time the diagnosis was X, and it responded to Y."
05 — Knowledge Graph
A multi-layered graph where patients, conditions, medications, evidence, and outcomes are all connected nodes. Agents don't query flat tables — they traverse relationships, discovering non-obvious connections your EHR would never surface. "Heart attack," "MI," "STEMI," and the ICD code I21.09 are all the same node.
Each patient is a connected subgraph — conditions, meds, labs, procedures, and outcomes linked by temporal and causal edges.
Guidelines, trials, and case reports are citable nodes. Every recommendation edge is weighted by evidence class and recency. Contradictions are flagged, not hidden.
Medical terminologies unified into a single semantic layer. Queried consistently across all agents — no ambiguity, no missed synonyms.
Proof of Intelligence
Real cases where the system identified what humans missed — not because it's better than you, but because it never blinks and it never forgets. These are the moments that matter.
Security & Compliance
HIPAA-compliant by architecture — not by policy alone. Your data never trains our models. Your patients' privacy is non-negotiable and technically enforced, not just promised.
Patient data is tokenized (names and identifiers replaced with codes) before it ever enters our system. It's processed in temporary memory and deleted immediately after your response. No patient data is ever stored in our infrastructure or used to improve our models.
By the Numbers
These aren't marketing numbers. They're the metrics that matter in clinical practice.
Multi-agent consensus with evidence grounding. General-purpose LLMs: 17–45% error rate in clinical contexts.
Every recommendation links to source evidence, patient data point, and confidence calculation. Nothing is a black box.
Pre-computed patient profiles enable sub-second response. No loading screens when a patient needs a decision now.
Multi-agent architecture handles 80 simultaneous clinical tasks at 65× less compute than single large-model systems.
We are not just assisting doctors — we are building the clinical reasoning infrastructure layer for healthcare. An ecosystem of autonomous agents that wake up, connect, remember, and act across your entire healthcare environment.
Agent Lifecycle
Agents aren't always running. They wake up when needed — triggered by clinical events, temporal patterns, or your explicit requests. Click each phase to explore it.
Agent exists as a configuration — watching for trigger conditions. Zero compute cost. 500 agents can monitor 10,000 patients in this state.
Trigger detected → Agent instantiates in <200ms → Pulls patient context → Loads relevant memory → Begins reasoning.
Agent performs its specialty task — differential generation, drug interaction check, risk calculation. Queries tools, reads memory, collaborates with other agents.
Recommendation delivered to physician. Agent state persisted to memory. Agent returns to dormant mode. Compute resources released. Audit trail complete.
Memory Architecture
Memory isn't an afterthought — it's the foundation. Click each layer to expand live clinical examples.
Real-time patient state — vitals, labs, active orders, current medications. Pre-computed vectors enable instant pattern matching. Survives for duration of encounter.
This admission's narrative — every decision, override, near-miss, and outcome. Progressive summarization compresses 72 hours into key clinical pivots. Survives 30 days.
Long-term learning — your personal clinical patterns, institutional protocols, and evidence base. Progressive summaries distilled weekly, monthly, quarterly. Survives indefinitely.
Tool Integration
Agents don't work in isolation. They call tools — EHR queries, lab systems, imaging databases, calculation engines, communication platforms.
Clinical Workflows
The Agent Harness adapts to clinical context. Inpatient agents monitor continuously. Outpatient agents think ahead and ensure nothing falls through cracks.
Ecosystem Integration
The Agent Harness isn't a standalone product — it's an infrastructure layer that integrates with everything you already use. Hover or click any node to learn more.
Modern, standardized data exchange. Real-time bi-directional sync with any FHIR-enabled EHR.
RESTful APIs for custom connections. Webhook support for event-driven activation.
Agents surface directly in your EHR interface — no separate login, no context switching.
IP rounds via mobile app. Bedside voice capture. Alerts via smartwatch.
Pricing
No enterprise sales cycles. No implementation fees. Self-serve pricing that individual doctors can expense without asking their hospital's IT department for permission.