CitiusTech is launching CitiusTech Knewron, a healthcare native AI platform, for healthcare organizations to integrate into their practices.
CitiusTech Knewron combines layers of healthcare domain context, multi-agent orchestration, and policy-based guardrails, the company said. These characteristics help organizations develop AI-first value stream maps of system workflows, create AI-augmented domain native product development life cycles (PDLCs), and modernize legacy systems, according to the firm.
CitiusTech highlighted the following CitiusTech Knewron features:
- Healthcare domain nativity that ingests and codifies clinical, Payer, and administrative workflows, so AI agents operate with curated, context-rich data. It also integrates with healthcare native stacks and tools through standard protocols.
- Agentic PDLC orchestration that serves as a visual workflow designer, custom agent builder, and agent-to-agent orchestration to automate product discovery, development, testing, and release.
- Policy-as-code and explainability with built-in guardrails, policy enforcement, and immutable audit trails for traceability and compliance.
- Model-agnostic gateway and cost awareness via multi-large language model support with cost-aware routing.
- Enterprise deployment options to support secure deployment in customer virtual private clouds, private clouds, or on-premises data centers.
The company plans future releases that will include several features, including the following: prebuilt domain-centric studios for agentic healthcare workflows, a healthcare “cognitive architecture” spanning perception, knowledge, reasoning, learning, orchestration, guardrails, and observability. CitiusTech added that these releases will also have actionable AI-Ops dashboards powered by FinOps algorithms and agentic lifecycle management.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
