Generative-AI Transforms Healthcare Charting

One of the most remarkable capabilities of Generative-AI is its ability to seemingly combine disparate and intricate sources of data, resulting in the creation of comprehensive and valuable collections of information and knowledge. These tools have access to an extensive and diverse range of information sources, primarily the vast global web and its infinite sources of materials. However, this large-scale and generalized approach often encounters challenges when attempting to apply it to more specialized knowledge domains.

Pharmaceutical research involves complex scientific literature encompassing extremely complex concepts in chemistry, biology, and medicine. The material is replete with specialized terminology and extensive contextually derived information. Consequently, these systems require reading broader and deeper into the source materials rather than focusing solely on potentially simpler words.

These general-purpose LLMs are trained on diverse datasets, demonstrating their potential applications. However, many of these systems currently lack the specialized knowledge necessary to comprehend nuanced scientific concepts. Even if they undergo fine-tuning on specific datasets, they may not fully grasp the depth of domain-specific terminologies, ontologies, and the intricate relationships between biological processes and drug mechanisms.