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The explosion of biomedical big data and information in the past decade created new opportunities for discoveries to improve the treatment and prevention of human diseases. But the large body of knowledge presents a grand new challenge. Individual scientists around the world are increasingly finding themselves overwhelmed by the sheer volume of research literature and are struggling to keep up to date and to make sense of this wealth of textual information.

This webinar summarizes current trends and use cases that apply state-of-the-art natural language processing in healthcare systems, pharmaceuticals, and health IT companies. David Talby covers unlocking use cases that become possible with current large language models; enabling medical domain experts to train & tune models in a co-code environment; and testing NLP models across aspects of Responsible AI in preparation for regulatory oversight. Talby also shares free and open-source tools that you can use today in these three areas.

Multiple studies have already demonstrated the effectiveness of electronic health record data in building Type 2 Diabetes Mellitus screening and prediction models. Through collaboration with family medicine physicians, nurse practitioners, patients, and computer science experts, the team at the University of Maribor aims to bring the T2DM prediction models closer to healthcare experts.

Join the Natural Language Processing Working Group for an energetic discussion of the article Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models by Tiffany Kung et al.

Dr. Xia (Ben) Hu presents a systematic framework from modeling and application perspectives for generating DNN interpretability, aiming at dealing with main technical challenges in interpretable machine learning, i.e., faithfulness, understandability and the efficiency of interpretability.

New advances are constantly made in biomedical and health natural language processing, but very few of these advances translate to measurable impact in medicine. When new AI and NLP methodologies are used in practice, they often magnify social biases or exhibit other undesirable behaviors. These failures of translation and AI-related injustices stem from a common source.