MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202611049099 A) filed by Vaibhav Dhor; Pranyush Jha; and Shashi Kant, Greater Noida, Uttar Pradesh, on April 17, for 'ai driven framework for deploying predictive and generative artificial intelligence in clinical environments.'
Inventor(s) include Vaibhav Dhor; Pranyush Jha; and Shashi Kant.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "An approach and framework is revealed to accountable execution of artificial intelligence in healthcare surroundings. The invention feeds on healthcare workflow data, organisational context, stakeholder input, technical constraints, and clinical data, and processes those through an implementation framework to identify adoption obstacles and prescribe evidence-based implementation strategies. The system will have a clinical decision support predictive AI engine and a generative AI engine which will be used in automated clinical documentation. Structured medical variables can be used by the predictive engine to determine treatment eligibility, patient risk, or diagnostic suggestions. The generative engine processes unstructured transcripts or text showing consultation into structured clinical and SOAP note records and summaries. Established governance is a layer that oversees deployed models with regard to fairness, privacy, explainability, compliance, drift, action regarding overrides, and user acceptance. Outputs can be edited or certified by human reviewer before being incorporated into patient records. The data of continuous monitoring is also utilized to optimize the implementation strategies as well as model performance over time. The invention also offers readiness scoring, workflow alignment recommendations, intervention planning, retraining controls, and deployment records which are audible. It can be a hospital platform or a cloud service, EHR plug-in, or telehealth assistant. The invention bridges the divide between experimental AI models and practical clinical implementation by integrating the principles of implementation science with actual AI services. It enhances adoption, minimizes the administrative load, builds clinician trust, and improves care to the patient, maintaining accountability and regulatory congruence. The revealed invention thus offers an expediency technical model of scalable and safe and evidence based implementation of artificial intelligence into healthcare systems."
Disclaimer: Curated by HT Syndication.
