Built for real health-system deployment. We help healthcare teams design, evaluate, and deploy clinical AI workflows, analytics platforms, RAG systems, and AI governance frameworks.
Teams can build models. Few can ship governed, monitored, clinically validated systems inside real hospital workflows. InsytAI closes that gap — from use-case discovery through production deployment and ongoing governance.
InsytAI sits between clinical research, data science, and production deployment. We do not just build AI prototypes; we help healthcare teams turn AI ideas into validated, governed, and usable workflows.
High-value capabilities for healthcare, research, and enterprise teams — each scoped for real deployment, not slide decks.
For hospitals & research groups
Problem: Risk models and imaging AI stall at validation — never reaching governed clinical use.
Solution: End-to-end clinical prediction pipelines with validation protocols and deployment architecture.
Proof: CalSense+ · Scoliosis AI
Review my AI use case →For ops & data teams
Problem: Clinical data is siloed — analytics and cohorting tools never reach frontline teams.
Solution: Cohorting, patient journey analytics, and dashboard platforms built on governed data infrastructure.
Proof: Discovery AI Platform
Assess my data platform →For clinical & admin teams
Problem: Generic chatbots fail on clinical documents — unsafe, ungrounded, unusable in workflows.
Solution: Governed RAG pipelines, medical document QA, summarization, and clinical copilot workflows.
Proof: RUSSELL GPT · MRI Spine LLM
Evaluate my LLM workflow →For leadership & governance teams
Problem: AI systems ship without monitoring, documentation, or ISO 42001-aligned governance.
Solution: Model risk review, evaluation frameworks, monitoring strategies, and governance documentation.
Proof: Responsible AI commitment
Request governance review →Discover → Design → Validate → Deploy → Govern — scoped for clinical safety, governance, and institutional fit.
Use-case definition, stakeholder mapping, clinical workflow review, data-readiness check, and risk framing.
Architecture design, data-flow mapping, model/RAG approach, privacy and security assumptions, and integration plan.
Evaluation metrics, bias and subgroup checks, clinical review loop, error analysis, usability review, and safety checks.
API or dashboard deployment, containerization, cloud or local deployment, access control, logging, and operational handover.
Monitoring, documentation, model cards, audit trail, update cycle, and post-deployment review.
InsytAI does not only build models. We help align the clinical, technical, and governance work required to move healthcare AI from prototype to safe deployment.
Workflow · Safety · Usability · Clinical validation
Data quality · Modelling · Analytics · Evaluation
Security · Deployment · Access control · Monitoring · Auditability
Translation · Implementation · Governance-ready delivery
Scoped engagements with clear deliverables — designed for healthcare AI timelines.
Review data, workflows, risks, and AI opportunities across your organisation.
Output: Use-case roadmap · workflow map · data-readiness review · risk register · implementation recommendation
Start a Project →Build proof-of-concept model, RAG workflow, or analytics tool with evaluation.
Output: Working prototype · evaluation report · safety and governance notes · next-step deployment plan
Start a Project →Containerised, monitored, governed system integrated into clinical workflows.
Output: Containerised app or workflow · API/dashboard integration · access-control model · monitoring plan · documentation · deployment-readiness package
Start a Project →Review model risk, evaluation, documentation, monitoring, and ISO 42001 alignment.
Output: Model risk review · evaluation framework · governance checklist · monitoring recommendations · ISO 42001-aligned gap review · documentation pack
Request Governance Review →19+ AI and data systems delivered or contributed to across healthcare, research, and health-system innovation environments — from clinical LLMs to medical imaging and mixed-reality tools. Selected systems have supported clinical, research, or operational workflows.
Fine-tuned clinical LLM for discharge summary generation. Published as a real-world RUSSELL GPT pilot study in The Lancet Regional Health–Western Pacific 51 (2024).
AI detection of calcium abnormalities for hyperparathyroidism diagnosis and treatment planning. $3.97M MOH-funded. CO-PI.
Medical voice transcription and clinical data reformatting platform. Converts unstructured voice notes into structured clinical records.
Secure institutional LLM for MRI spine request form enhancement and auto-protocoling. Published in The Spine Journal 25(3), 505-514 (2025).
Smart Modelling and Intelligent Learning for Enhancing Oral Health. Automated oral health diagnosis platform for population-level screening.
Published mixed-reality venipuncture aid using CNNs and semi-supervised learning, with HoloLens vein overlay for guided IV access.
Portable mixed-reality 3D ultrasound tracking, reconstruction and overlay system. Published at ASMUS/MICCAI 2023 and in Lecture Notes in Computer Science.
System architect for production-grade clinical AI deployment platform integrating with hospital cluster EMR, data governance SOPs, and multi-department data feeds.
Published automated Cobb angle measurement for scoliosis radiographs, with GAN-based vertebrae segmentation and deep learning-assisted productivity work.
Machine-learning workflow to optimise clinic and surgical scheduling, supporting operational planning and resource allocation.
Cohort study examining allergic disease risk across ancestry and early-life environmental contexts in Singapore and Australia.
Neuroimaging and machine-learning study using brain connectivity features to support cognitive-risk stratification in preterm children.
Clinical prediction work evaluating severity scoring for acute pancreatitis and supporting risk stratification in hepatobiliary care.
Prediction model to estimate risk of positive blood cultures during chemotherapy-related oncology care.
Transcriptomics-based risk modelling to identify patients with multiple myeloma at high risk of induction failure or early relapse, supporting research into functional high-risk disease biology.
Unified breast cancer research database with NLP tools to extract structured data from pathology reports, imaging notes, and clinical documentation.
Machine-learning workflow to enrich breast-cancer screening cohorts for early-detection research and prioritisation.
Healthcare AI deployment platform enabling integration between institutional clinical systems and locally developed AI applications, with governance, access control, and multi-department workflow support.
On-premises high-performance computing platform enabling secure deep-learning workloads for sensitive healthcare data within institutional boundaries.
InsytAI is led by a PhD data scientist with over a decade of experience building healthcare AI, clinical analytics, and data platforms across healthcare, research, and health-system innovation environments. Public identity is intentionally limited during the first phase, but scholarly and publication records are provided for verification.
Figures reflect research programme and grant involvement across funded healthcare AI work — not InsytAI corporate funding.
Experience across healthcare, research, and health-system innovation environments
SMILE AI programme featured as part of Singapore's national supercomputing initiative.
CalSense project — AI-assisted clinical record management reducing patient waiting times.
International feature on SMILE AI's impact on population-level oral health screening.
Every InsytAI engagement is scoped, governed, and delivered to production standards — not proof-of-concept standards. Healthcare AI that can't be audited, monitored, or explained isn't safe to ship.
De-identified screenshots, interface visuals, and deployment artefacts from healthcare AI systems in practice.
A practical checklist covering data readiness, clinical workflow fit, model evaluation, privacy/security, governance, monitoring, and implementation risk.
Monthly practical notes on healthcare AI deployment, governance, RAG/LLM workflows, and clinical data platforms — with a Singapore and regional health-system focus.
Checklist sent. Want to benchmark your current AI governance setup against it?
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Book Diagnostic Call Download Checklist (HTML)Work with InsytAI on clinical AI systems, healthcare analytics platforms, RAG/LLM workflows, and AI governance reviews.
Work with InsytAI on clinical AI systems, healthcare analytics platforms, RAG/LLM workflows, and AI governance reviews. Book a discovery call, start a project brief, or download the deployment readiness checklist.
InsytAI provides AI, analytics, and technical consulting. We do not provide medical advice, diagnosis, or treatment recommendations.
We work with healthcare, research, and enterprise teams building safe, practical AI and data workflows.