  In this paper, we introduce InMD-X, a collection of multiple large language
models specifically designed to cater to the unique characteristics and demands
of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking
development in natural language processing, offering a suite of language models
fine-tuned for various aspects of the internal medicine field. These models
encompass a wide range of medical sub-specialties, enabling IMDs to perform
more efficient and accurate research, diagnosis, and documentation. InMD-X's
versatility and adaptability make it a valuable tool for improving the
healthcare industry, enhancing communication between healthcare professionals,
and advancing medical research. Each model within InMD-X is meticulously
tailored to address specific challenges faced by IMDs, ensuring the highest
level of precision and comprehensiveness in clinical text analysis and decision
support. This paper provides an overview of the design, development, and
evaluation of InMD-X, showcasing its potential to revolutionize the way
internal medicine practitioners interact with medical data and information. We
present results from extensive testing, demonstrating the effectiveness and
practical utility of InMD-X in real-world medical scenarios.
