oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness

Large Language Models (LLMs) show potential for medical applications but often lack specialized clinical knowledge. Retrieval Augmented Generation (RAG) allows customization with domain-specific information, making it suitable for healthcare. This study evaluates the accuracy, consistency, and safety of RAG models in determining fitness for surgery and providing preoperative instructions. We developed LLM-RAG models using 35 local and 23 international preoperative guidelines and tested them against human-generated responses. A total of 3,682 responses were evaluated. Clinical documents were processed using Llamaindex, and 10 LLMs, including GPT3.5, GPT4, and Claude-3, were assessed. Fourteen clinical scenarios were analyzed, focusing on seven aspects of preoperative instructions. Established guidelines and expert judgment were used to determine correct responses, with human-generated answers serving as comparisons. The LLM-RAG models generated responses within 20 seconds, significantly faster than clinicians (10 minutes). The GPT4 LLM-RAG model achieved the highest accuracy (96.4% vs. 86.6%, p=0.016), with no hallucinations and producing correct instructions comparable to clinicians. Results were consistent across both local and international guidelines. This study demonstrates the potential of LLM-RAG models for preoperative healthcare tasks, highlighting their efficiency, scalability, and reliability.

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.

Towards Clinical AI Fairness: Filling Gaps in the Puzzle

The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from healthcare. Despite this, a significant disconnect persists between technical advancements and their practical clinical applications, resulting in a lack of contextualized discussion of AI fairness in clinical settings. Through a detailed evidence gap analysis, our review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. Additionally, our analysis highlights a substantial reliance on group fairness, aiming to ensure equality among demographic groups from a macro healthcare system perspective; in contrast, individual fairness, focusing on equity at a more granular level, is frequently overlooked. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities. Beyond applying existing AI fairness methods in healthcare, we further emphasize the importance of involving healthcare professionals to refine AI fairness concepts and methods to ensure contextually relevant and ethically sound AI applications in healthcare.

Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties

Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large Language Model (LLM) as a Clinical Decision Support System (CDSS) for safe medication prescription. This model addresses the limitations of traditional rule-based CDSS by providing relevant prescribing error alerts tailored to patient context and institutional guidelines. Objective: The study evaluates the efficacy of an LLM-based CDSS in identifying medication errors across various medical and surgical case vignettes, compared to a human expert panel. It also examines clinician preferences among different CDSS integration modalities: junior pharmacist, LLM-based CDSS alone, and a combination of both. Design, Setting, and Participants: Utilizing a RAG model with GPT-4.0, the study involved 61 prescribing error scenarios within 23 clinical vignettes across 12 specialties. An expert panel assessed these cases using the PCNE classification and NCC MERP index. Three junior pharmacists independently reviewed each vignette under simulated conditions. Main Outcomes and Measures: The study assesses the LLM-based CDSS's accuracy, precision, recall, and F1 scores in identifying Drug-Related Problems (DRPs), compared to junior pharmacists alone or in an assistive mode with the CDSS. Results: The co-pilot mode of RAG-LLM significantly improved DRP identification accuracy by 22% over solo pharmacists. It showed higher recall and F1 scores, indicating better detection of severe DRPs, despite a slight decrease in precision. Accuracy varied across categories when pharmacists had access to RAG-LLM responses. Conclusions: The RAG-LLM based CDSS enhances medication error identification accuracy when used with junior pharmacists, especially in detecting severe DRPs.

Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report

Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs. This case study presents the development and evaluation of an LLM-RAG pipeline tailored for healthcare, focusing specifically on preoperative medicine. Methods: We developed an LLM-RAG model using 35 preoperative guidelines and tested it against human-generated responses, with a total of 1260 responses evaluated. The RAG process involved converting clinical documents into text using Python-based frameworks like LangChain and Llamaindex, and processing these texts into chunks for embedding and retrieval. Vector storage techniques and selected embedding models to optimize data retrieval, using Pinecone for vector storage with a dimensionality of 1536 and cosine similarity for loss metrics. Human-generated answers, provided by junior doctors, were used as a comparison. Results: The LLM-RAG model generated answers within an average of 15-20 seconds, significantly faster than the 10 minutes typically required by humans. Among the basic LLMs, GPT4.0 exhibited the best accuracy of 80.1%. This accuracy was further increased to 91.4% when the model was enhanced with RAG. Compared to the human-generated instructions, which had an accuracy of 86.3%, the performance of the GPT4.0 RAG model demonstrated non-inferiority (p=0.610). Conclusions: In this case study, we demonstrated a LLM-RAG model for healthcare implementation. The pipeline shows the advantages of grounded knowledge, upgradability, and scalability as important aspects of healthcare LLM deployment.

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist

The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (AI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare. However, less clear is how to resolve such issues beyond following guidelines and regulations that are still under discussion and development. On the other hand, other types of generative AI have been used to synthesize images and other types of data for research and practical purposes, which have resolved some ethical issues and exposed other ethical issues, but such technology is less often the focus of ongoing ethical discussions. Here we highlight gaps in current ethical discussions of generative AI via a systematic scoping review of relevant existing research in healthcare, and reduce the gaps by proposing an ethics checklist for comprehensive assessment and transparent documentation of ethical discussions in generative AI development. While the checklist can be readily integrated into the current peer review and publication system to enhance generative AI research, it may also be used in broader settings to disclose ethics-related considerations in generative AI-powered products (or real-life applications of such products) to help users establish reasonable trust in their capabilities.

Towards clinical AI fairness: A translational perspective

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.