Comparative Analysis of Drug-GPT and ChatGPT LLMs for Healthcare Insights: Evaluating Accuracy and Relevance in Patient and HCP Contexts (2307.16850v1)

Abstract: This study presents a comparative analysis of three Generative Pre-trained Transformer (GPT) solutions in a question and answer (Q&A) setting: Drug-GPT 3, Drug-GPT 4, and ChatGPT, in the context of healthcare applications. The objective is to determine which model delivers the most accurate and relevant information in response to prompts related to patient experiences with atopic dermatitis (AD) and healthcare professional (HCP) discussions about diabetes. The results demonstrate that while all three models are capable of generating relevant and accurate responses, Drug-GPT 3 and Drug-GPT 4, which are supported by curated datasets of patient and HCP social media and message board posts, provide more targeted and in-depth insights. ChatGPT, a more general-purpose model, generates broader and more general responses, which may be valuable for readers seeking a high-level understanding of the topics but may lack the depth and personal insights found in the answers generated by the specialized Drug-GPT models. This comparative analysis highlights the importance of considering the LLM's perspective, depth of knowledge, and currency when evaluating the usefulness of generated information in healthcare applications.

Comparative Analysis of Drug-GPT™ and ChatGPT LLMs for Healthcare Insights: Evaluating Accuracy and Relevance in Patient and HCP Contexts

The paper under discussion offers a systematic comparison of three Generative Pre-trained Transformer (GPT) models—Drug-GPT™ 3, Drug-GPT™ 4, and ChatGPT—in the specialized setting of healthcare-related question answering (Q&A). The primary aim is to ascertain which model excels in delivering accurate and pertinent information concerning patient experiences with atopic dermatitis and healthcare professional (HCP) discussions about diabetes.

Core Findings

The analysis demonstrates that, although all models can generate relevant and credible responses, Drug-GPT™ 3 and Drug-GPT™ 4, both supported by curated datasets of patient and HCP social media and message board posts, contribute more focused and nuanced insights. Conversely, ChatGPT, primarily a general-purpose LLM, tends to produce broader overviews that may be suitable for a general understanding but might lack the specifics and depth of specialized insights provided by Drug-GPT™ models.

Methodology and Experiments

The paper employs a Q&A setting to test each model. Two specific scenarios are examined:

1. Patients: The models address challenges associated with living with atopic dermatitis.
2. HCPs: The focus is on what themes HCPs discuss in relation to diabetes.

For evaluation, emphasis is placed on the relevance and accuracy of model responses, with hyperparameters like temperature set to enhance deterministic output. The curated datasets for Drug-GPT™ models are pivotal in harnessing domain-specific knowledge that enhances the relevance and accuracy beyond what general models such as ChatGPT can offer.

Numerical Results and Analysis

In the first experiment dealing with patient experiences regarding atopic dermatitis, Drug-GPT™ models were notably aligned with specific patient challenges, such as the effects of long-term topical steroid usage and managing dietary triggers. These responses suggest a more intimate understanding derived from real patient discussions. ChatGPT, albeit accurate and coherent, provided a more generic outline of challenges like skin irritation and treatment difficulties.

In diabetes-related discussions among HCPs, Drug-GPT™ models articulated specific medical themes and interventions validated by direct quotes from real-world HCP communications. ChatGPT outlined wider themes such as prevention and lifestyle management, underpinned by hypothetical sources rather than real dialogues.

Implications for Healthcare

The paper's findings underscore the importance of specialized LLMs tailored for particular domains like healthcare. Drug-GPT™ models demonstrate superior ability to assimilate domain-specific data, thereby offering more relevant and actionable insights for both patients and healthcare practitioners. This marks a critical step in leveraging AI for specialized applications, distinguishing it from a one-size-fits-all approach typified by general models like ChatGPT.

The implications for healthcare could be substantial, offering improved support for clinical decision-making, patient education, and personalized healthcare strategies. GPT models fine-tuned with domain-rich data can significantly enhance the utility of AI in healthcare settings, offering a depth of perspective unavailable in unspecialized models.

Future Developments

Looking forward, integrating AI into healthcare necessitates balancing specialized model training with ethical considerations. While the presence of domain-specific datasets enriches AI utility, attention to biases, data privacy, and the authenticity of AI-generated responses is crucial. Future AI developments could explore hybrid models combining the strengths of general and specialized models, further refining their relevance and applicability across diverse healthcare scenarios.

In conclusion, this paper presents an insightful evaluation of the comparative performance of specialized versus general LLMs in healthcare contexts. Drug-GPT™ models, by leveraging domain-specific datasets, present a strong case for specialized AI solutions that cater directly to the nuanced needs of specific fields, significantly improving the value and applicability of AI in healthcare.