Abstract: A method and system for automated continuous validation for regulatory compliance of CS with dynamic component. On identification of learning in the CS, a User Acceptance Testing (UAT) is performed using automated test cases of varying types in accordance with what-if scenarios and synthetic data generated using a unique approach. Thereafter, a base validation testing of the CS is performed with clean data (positive scenarios of outcome of the CS) and dirty data (negative scenarios) by conducting repeatability, stability (consistency) and reliability checks. The base validation testing is then followed by learning saturation testing on only if the dynamic component is validated, is rolled out in production environment else is rolled back to the earlier version.

Abstract: This disclosure relates to a system and method for custom made medical devices life cycle management, the life cycle management of the medical devices include tracking of material and procedure to be used in additive manufacturing of the medical device and analyzing design risk as per the raw material and the design specification. Further, it includes to identify potential hazards based upon the materials, their interactions and effect on process to be followed. An artificial intelligence (AI) enabled model is trained with one or more sample datasets pertaining to a plurality of materials to be used in the additive manufacturing to audit design, to determine materials to be used, and to generate a unique code to be used to label the manufactured medical device. A smart contract of blockchain is configured to record received input and update with new points for regulatory compliance.

Abstract: State of art techniques hardly provide solutions for validation testing of Computer Systems (CS) with dynamic components that undergo frequent changes or learnings. Embodiments of the present disclosure provide a method and system for automated continuous validation for regulatory compliance of CS with dynamic component. On identification of learning in the CS, a User Acceptance Testing (UAT) is performed using automated test cases of varying types in accordance with what-if scenarios and synthetic data generated using a unique approach. Thereafter, a base validation testing of the CS is performed with clean data (positive scenarios of outcome of the CS) and dirty data (negative scenarios) by conducting repeatability, stability (consistency) and reliability checks. The base validation testing is then followed by learning saturation testing on only if the dynamic component is validated, is rolled out in production environment else is rolled back to the earlier version.

Abstract: This disclosure relates generally to clinical trial management, and more particularly to method of performing a data driven cognitive clinical trial feasibility analysis. In one embodiment, the method comprising (a) receiving, a plurality of protocol requirements to initiate a clinical trial site feasibility; (b) identifying, a plurality of meta-data for at least one protocol requirement from the plurality of protocol requirements; (c) obtaining, an exhaustive list of historic clinical trial site data for the identified meta-data from a site data repository; (d) obtaining, an exhaustive list of third party clinical trial site data for identified meta-data from a third party data repository; and (e) assessing, the exhaustive list of clinical trial site data and the exhaustive list of third party clinical trial site data to obtain a list of identified clinical trial site feasibility.

Abstract: This disclosure relates to a system and method for custom made medical devices life cycle management. the life cycle management of the medical devices include tracking of material and procedure to be used in additive manufacturing of the medical device and analyzing design risk as per the raw material and the design specification. Further, it includes to identify potential hazards based upon the materials, their interactions and effect on process to be followed. An artificial intelligence (AI) enabled model is trained with one or more sample datasets pertaining to a plurality of materials to be used in the additive manufacturing to audit design, to determine materials to be used, and to generate a unique code to be used to label the manufactured medical device. A smart contract of blockchain is configured to record received input and update with new points for regulatory compliance.

Abstract: This disclosure relates generally to clinical trial management, and more particularly to method of performing a data driven cognitive clinical trial feasibility analysis. In one embodiment, the method comprising (a) receiving, a plurality of protocol requirements to initiate a clinical trial site feasibility; (b) identifying, a plurality of meta-data for at least one protocol requirement from the plurality of protocol requirements; (c) obtaining, an exhaustive list of historic clinical trial site data for the identified meta-data from a site data repository; (d) obtaining, an exhaustive list of third party clinical trial site data for identified meta-data from a third party data repository; and (e) assessing, the exhaustive list of clinical trial site data and the exhaustive list of third party clinical trial site data to obtain a list of identified clinical trial site feasibility.