Seegnal Names Yura Zharkovsky VP of AI to Scale Its Prescription Intelligence Platform
Seegnal Inc. (TSXV: SEGN) has appointed Yura Zharkovsky as Vice President of Artificial Intelligence at Seegnal eHealth Ltd., effective January 4, 2026. The move strengthens Seegnal's plan to build the Intelligence Layer for prescriptions and accelerate Seegnal Guard, its AI-enhanced, clinician-led system for safer, consistent, and cost-aware prescribing.
Zharkovsky will lead the evolution of Seegnal's AI stack with a clear mandate: push predictive and personalization capabilities while keeping clinicians in control. The priority is practical AI that fits real workflows, explains its reasoning, and earns trust at scale.
Why this matters for IT and development teams
- Interoperability by default: Expect deep EHR integration and event-driven workflows that pull patient context at the point of care. FHIR/HL7 alignment and low-latency services will be essential for real-time checks and explainable outputs. See FHIR for the common data model.
- Multimodal data pipelines: Genetics, labs, ECG, allergies, demographics, smoking status, food interactions, and concomitant meds require high-quality ingestion, standardization, and de-duplication. A governed feature store and schema versioning will reduce breakage across deployments.
- Human-in-the-loop AI: Seegnal's principle is augmentation, not replacement. That implies clear explanations, decision trails, override reasons, and feedback loops from clinicians that feed continuous learning without compromising safety.
- Alert fatigue reduction: The system aims to cut noise (>90% reduction reported) and deliver precision alerts (up to 98% accuracy cited). For builders, that means careful thresholding, context-aware rules, and model ensembles tested against real-world prescribing behavior-not just benchmark datasets.
- Governance and auditability: Full model lineage, dataset cards, bias checks, and drift monitoring are table stakes in clinical environments. Every recommendation must be reproducible, logged, and ready for audit.
- Deployment realities: Hospitals often need on-prem or hybrid setups, strict PII controls, and data residency assurances. Latency budgets must respect clinician workflow, not the other way around.
- Measurable outcomes: Tie releases to operational metrics: time saved on renewals, reduction in preventable ADEs, false-positive cuts, and clinician adoption. A/B with guardrails, offline replay testing, and phased rollouts will lower risk.
The leadership move
Zharkovsky previously led AI at NeuroKaire, where he helped build systems to assist treatment selection for major depressive disorder. His background spans applied ML and data science leadership focused on moving complex clinical data into reliable, deployed tools.
He is known for strong engineering standards, ethics, and collaboration-aligned with Seegnal's emphasis on transparency, clinical accountability, and adoption in real care settings.
What Seegnal's building
Seegnal Guard operates as a prescription intelligence layer that integrates patient-specific data at the point of care while minimizing disruptions. Clinicians get fewer, higher-precision alerts, keep their workflow intact, and maintain control over decisions.
Seegnal reports clinician time savings, fewer admissions, lower medication consumption, and precision alerts reaching up to 98% accuracy. In Israel, over 10,000 clinicians use the platform daily, and the Ministry of Health has adopted Seegnal's patient-specific standard in governmental hospitals.
Leadership perspectives
"I am thrilled to welcome Yura Zharkovsky to the leadership team," said Elad Bibi-Aviv, CEO of Seegnal. "Having worked with Yura for the last two years, I have seen firsthand his ability to build healthcare technology at the intersection of clinical practice and data execution. He is the ideal leader to help us turn prescribing into a measurable, AI lead, governable capability across the enterprise."
Mr. Zharkovsky added: "Joining Seegnal is a natural progression of the work Elad and I have been focused on. Seegnal is unique because it isn't just a concept; it is a platform built on vast real-world prescribing data and the actual behaviors of clinicians in workflow. I look forward to scaling Seegnal Guard and the broader Intelligence Layer to ensure that every prescription decision is safer, more consistent and more aligned with both patient outcomes and system-wide value."
About Seegnal
Seegnal targets Adverse Drug Effects (ADEs), a major source of harm globally. Its SaaS clinical decision support platform integrates patient-specific signals-genetics, lab results, ECG, allergies, lifestyle factors, age, gender, and polypharmacy-while reducing alert load for clinicians by more than 90%.
The company is marketing in Israel, the UAE, the UK, the US, and Poland. The platform is a standard-of-care system across Israel and is used daily by thousands of clinicians.
For builders: immediate takeaways
- Design for explainability first: present causal factors, references, and side-effect tradeoffs clearly.
- Treat clinical data as dynamic: version schemas, track provenance, and simulate edge cases before go-live.
- Close the loop with clinicians: capture feedback signals at every decision point to improve relevance over time.
- Measure what matters: reduce false positives, shorten prescribing time, and improve adherence to local policies.
Cautionary note
Statements about development strategy and expected outcomes are forward-looking and involve risks and uncertainties. Actual results may differ. For risk factors and disclosures, see Seegnal's filings on SEDAR+.
Further reading
- Technical standard: HL7 FHIR
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