Text summarization for the clinical data
Clinical data summarization using generative AI involves leveraging advanced algorithms to extract, analyze, and condense vast amounts of medical information into concise, actionable insights.
This technology employs natural language processing (NLP) to understand and interpret clinical narratives, enabling the generation of summaries that highlight crucial patient data, trends, and outcomes.
By automating the summarization process, generative AI reduces the time and effort required by healthcare professionals to review patient records, enhances decision-making accuracy, and improves patient care. It ensures that critical information is readily accessible, facilitating more efficient and informed clinical workflows.
Arasu Narayan
Sr.Principal Data Scientist at Oracle Inc.
With two decades of experience, this accomplished Data Scientist excels in machine learning, algorithm design, statistical modeling, and large-scale data processing. Their expertise in Business Intelligence (BI) and Artificial Intelligence (AI) tools enables them to solve complex problems and deliver substantial value through effective risk management and marketing strategies.
Proficient in NLP, and LLM, they specialize in Natural Language Processing (NLP), recommendation engines, and Time Series forecasting, successfully applying these skills across various domains. As a Technical Leader, they drive innovation, lead high-performing teams, and deliver measurable business impact through data-driven solutions.
