AI applied to the Pharmaceutical and Healthcare industry
The pharmaceutical and healthcare industries are faced with a series of challenges and opportunities in the field of information processing. The growing amount of data being collected in the healthcare sector - from electronic medical records to genomic data - presents an opportunity to extract high-value insights to improve the efficiency of processes and optimize health outcomes.
AI applied to the Pharmaceutical and Healthcare industry
Artificial Intelligence can be very effective in optimizing research and business processes in these industries in numerous use cases, such as in Research and Diagnostics, where AI is being used to analyze medical images for diagnosis, such as in tumor detection, or to analyze clinical trial data to gain insights from large biomedical datasets, or to discover and apply potential drugs, such as in RNA sequencing for personalized treatments, among others.
AI is also being applied to Management and Efficiency, for example in applications to predict drug demand or improve customer service (for example in hospitals). It can also be used in predictive maintenance (such as in factories or for medical devices), or in human resource management or knowledge management, among other applications.
However, AI still faces challenges and limitations that need to be overcome, especially when it comes to data quality and information scarcity, model interpretability, model development industrialization, or the lack of professional profiles with specific expertise and industry knowledge.
At Management Solutions, we are at the forefront of this transformation, enabling the pharmaceutical and healthcare industries to harness the full potential of AI. Our value proposition is built around four areas of work:
- AI model development, validation and testing. We help identify the right use cases for AI, with a focus on supporting the development of use cases where AI techniques are applied to the massive data available in research and diagnostic units. We also cover tasks related to model validation, compliance, interpretability, and fairness to ensure that AI models are not only effective, but also ethical and safe.
- AI adoption model: Thanks to our understanding of the context and IA regulation, we offer a tailored approach that takes into account each organization's appetite for AI risk and guides them in developing a comprehensive AI diagnosis and adoption plan.
- Data, infrastructure and architecture: We help institutions develop forward-looking technology roadmaps, leverage cloud services, and embrace the MLOps philosophy.By prioritizing data quality, traceability and integration, we ensure that AI models are built on a solid foundation.
- Model deployment and automation: We help organizations automate their modeling and validation processes using our cutting-edge proprietary tools - such as ModelCraft™ for AutoML, Gamma™ for model governance, and Hatari™ for natural language interpretation.