Artificial Intelligence (AI) is a subfield of computer science that deals with the analysis and replication of intelligent behavior. Machine learning (ML) is a subfield of artificial intelligence and deals with procedures (algorithms) that can automatically evaluate data for prediction or decision-making on a large scale.
Knowledge and experience are, after employees, a company's most valuable resource. Life sciences companies in particular manage large amounts of explicit knowledge in document repositories for purposes such as research, manufacturing, and the approval of drugs and medical devices. The use of artificial intelligence technologies offers considerable potential here for increasing data quality and optimizing processes.
Inconsult combines AI/ML expertise with years of life science industry experience to capture and realize the value of data along the entire value chain.
Life science companies manage large document repositories. The volume, the demands on the correctness of the information contained therein and the complexity of embedding this information in business processes represent a major challenge. At this point, the use of artificial intelligence offers considerable potential both for increasing data quality and for using the information it contains for automatic evaluation.
AI/ML technologies enable automatic digitization, categorization, and tagging of large document collections to transform unstructured data silos into intelligent data repositories. Automatic extraction of structured information such as names, active ingredients, and quantity information to create databases and quality assurance allows IDMP-compliant data to be made available to regulatory agencies. Document similarity analysis allows relationships to be established and similarity and redundancy to be detected.
A data strategy defines the long-term goals for creating value from corporate data as well as the activities and resources required to achieve them. Planning a data strategy begins with an inventory and an analysis of potential. This is followed by the identification of use cases and an assessment of their feasibility. The creation of a roadmap with identified use cases concludes the data strategy and forms the basis for the subsequent implementation phase. In this phase, the use cases are initially implemented as a proof of concept and, after positive evaluation, as a productive application.
Inconsult has the strategic and technological expertise to implement your life science AI/ML use case. We identify and evaluate use cases, create data pipelines, implement machine learning processes support the operation of your AI solution. Accelerate your processes, make more informed decisions, and improve your organization's data quality.