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Reg-GPTTM: A Conversational AI Model for Enhanced Decision-Making in Regenerative Medicine

Maharaj, D.; Zhang, W.

2025-01-20 bioinformatics
10.1101/2025.01.14.633051 bioRxiv
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BackgroundAs artificial intelligence (AI) continues to transform various aspects of our lives, conversational AI models have become increasingly sophisticated. The development of more accurate and informative language processing assistants has significant implications for numerous fields, including health care, medical service, and research assistance. Materials and MethodsReg-GPT was developed by the Maharaj Institute of Immune Regenerative Medicine (MIIRM) using a combination of supervised and unsupervised learning techniques. The LLaMa 3.1 models parameters were fine-tuned using vast amounts of text data, enabling Reg-GPT to learn from its interactions with users. ResultsOur evaluation shows that Reg-GPT model performs well in several key areas, including response accuracy, fluency, and engagement. The results highlight the potential benefits of integrating Reg-GPT into regenerative medicine (RM) applications. ConclusionThis article provides a comprehensive introduction to Reg-GPT, showcasing its capabilities, performance, and potential uses. We believe that Reg-GPT has the potential to provide significant value in the RM and Medicare fields.

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The top 11 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.