AI & ML
State of the art AI models
In an era where generic AI tools often fall short in the nuanced, high-stakes world of clinical medicine - Pensieve's AI & ML capabilities stand apart by delivering intelligence that is both globally informed and deeply personalized to your hospital's unique environment. At the core of Pensieve is a suite of advanced machine learning models pre-trained on millions of de-identified clinical records from diverse global datasets, ensuring broad, evidence-based knowledge from day one. These models power a sophisticated differential diagnosis engine that doctors access seamlessly via the AI chat agent or directly within template-based data entry. As a clinician enters chief complaints, history, examination findings, vitals, and early investigations through custom or pre-built templates (blood pressure trends, IP assessment sheets, fever charts), the engine analyzes the chronological timeline in real time, suggesting ranked differential diagnoses complete with probability scores, key distinguishing features, recommended investigations, and red flags for life-threatening conditions like sepsis, STEMI, or stroke. Suggestions draw from ICD-11 and SNOMED CT mappings already embedded in your diagnoses collection, aligning perfectly with your coding workflows. What truly sets Pensieve apart is its commitment to continuous, hospital-specific improvement through federated learning - a privacy-preserving technique that allows models to refine themselves using your own anonymized data without ever moving sensitive patient information off your secure cloud instance. As your doctors add diagnoses, orders, template entries, outcomes (resolved dates, follow-ups, readmissions), and notes across thousands of patient timelines, the system aggregates patterns locally - demographics, seasonal disease trends common in your region, antibiotic resistance profiles unique to your catchment area, or procedure outcomes influenced by your surgical teams. These insights are used to fine-tune the AI models incrementally, so differentials become more accurate for your patient mix (e.g., prioritizing dengue over influenza during monsoon in coastal India), drug suggestions favor your hospital formulary and local generics, and alerts align with your internal protocols. Beyond diagnosis, AI permeates documentation and prediction. Radiology report auto-generation transforms uploaded DICOM studies and doctor annotations into structured, narrative reports with findings, impressions, and SNOMED-coded conclusions in seconds - ready for review and e-signature. The discharge summary writer pulls directly from the patient's entire timeline: diagnoses with onset and resolution dates, orders and treatments administered, template-based progress notes, investigations with trends, and follow-up plans - producing a beautifully formatted, multilingual summary (English plus regional Indian languages) that requires only minor edits before finalization. Predictive analytics provide forward-looking intelligence: length-of-stay forecasts based on admission templates, comorbidity flags, and early vital trends help optimize bed management; readmission risk scores flag high-risk patients at discharge for intensified follow-ups or home monitoring via the Patient App; mortality predictors in ICU settings combine real-time early warning scores (NEWS2, qSOFA) with historical outcomes from your own data to guide escalation protocols. All models evolve safely - doctors provide feedback on suggestions (accept, reject, modify), which feeds back into fine-tuning loops while maintaining full compliance with India's DPDP Act and HIPAA readiness for US expansion. Over time, your hospital's AI becomes a digital reflection of your clinical excellence: a tool that understands not just global medicine, but the specific realities of your wards, your doctors' preferences, and your patients' needs - delivering smarter diagnosis, faster documentation, and proactive care that improves outcomes and efficiency from a single clinic today to a multi-site network tomorrow.