Knowledge Vision Company
Knowledge Vision is an innovative R&D company that specializes in pathological artificial intelligence-assisted analysis and is committed to using artificial intelligence to assist doctors in accurate companion diagnostics. The founder team of Knowledge Vision has more than seven years of experience in innovative medical device development related to machine learning, familiar with the classification criteria for medical devices, clinical trial specifications, production quality system management, registration processes and fee-related regulations. The company develops a cloud-free platform for research and development of codeless AI applications - AI VIEWER, which utilizes data encryption and authorization mechanisms to ensure user data security and facilitate multi-party cooperation and data sharing. By collecting pathological AI to analyze the commonality of clinical application requirements, standardize and modularize the technologies involved in the research and development of pathological AI, and realize the whole process of data management, image annotation, algorithm development and application release required for pathological AI application research and development. And qualitative, localized, quantitative and visualized, digital analysis of histological pathology. AI VIEWER can be widely used in the pathological analysis of new drug research and development, and is committed to improving the efficiency and quality of drug development CRO. In the process of new drug research and development, AI VIEWER can realize the auxiliary quantitative analysis of digital pathological images, provide visual prediction data for drug researchers, realize traceability, quality assurance and quality control of pathological analysis process, reduce drug development cost and clinical trial risk. To enhance the market competitiveness of drugs. At the same time, the pathological diagnosis results based on quantitative analysis provide intelligent drug-assisted decision-making for new drug researchers, determine the correlation between pathological data characteristics and prognosis, improve the efficiency and progress of quantitative analysis of pathological sections, and ensure the consistency of histopathological analysis. It can be used for screening of subjects and for the determination of concomitant diagnostic cutoff values.