The integration of Large Language Models (LLMs) into healthcare diagnostics
offers a promising avenue for clinical decision-making. This study outlines the
development of a novel method for zero-shot/few-shot in-context learning (ICL)
by integrating medical domain knowledge using a multi-layered structured
prompt. We also explore the efficacy of two communication styles between the
user and LLMs: the Numerical Conversational (NC) style, which processes data
incrementally, and the Natural Language Single-Turn (NL-ST) style, which
employs long narrative prompts. Our study systematically evaluates the
diagnostic accuracy and risk factors, including gender bias and false negative
rates, using a dataset of 920 patient records in various few-shot scenarios.
Results indicate that traditional clinical machine learning (ML) models
generally outperform LLMs in zero-shot and few-shot settings. However, the
performance gap narrows significantly when employing few-shot examples
alongside effective explainable AI (XAI) methods as sources of domain
knowledge. Moreover, with sufficient time and an increased number of examples,
the conversational style (NC) nearly matches the performance of ML models. Most
notably, LLMs demonstrate comparable or superior cost-sensitive accuracy
relative to ML models. This research confirms that, with appropriate domain
knowledge and tailored communication strategies, LLMs can significantly enhance
diagnostic processes. The findings highlight the importance of optimizing the
number of training examples and communication styles to improve accuracy and
reduce biases in LLM applications.

通过结合医学领域知识，采用多层结构的提示方法实现零 / 少样本上下文学习，并探索用户与大型语言模型的两种交流方式对诊断准确性和风险因素的影响。结果表明，大型语言模型通过域知识和定制化沟通策略能够显著提高诊断流程的准确性。研究还强调了在大型语言模型应用中优化训练样本数量和交流方式以提高准确性并减少偏见的重要性。

XAI4LLM。让机器学习模型和 LLM 合作，提升医疗领域的上下文学习

XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced  In-Context Learning in Healthcare

This study assesses the ability of state-of-the-art large language models
(LLMs) including GPT-3.5, GPT-4, Falcon, and LLaMA 2 to identify patients with
mild cognitive impairment (MCI) from discharge summaries and examines instances
where the models' responses were misaligned with their reasoning. Utilizing the
MIMIC-IV v2.2 database, we focused on a cohort aged 65 and older, verifying MCI
diagnoses against ICD codes and expert evaluations. The data was partitioned
into training, validation, and testing sets in a 7:2:1 ratio for model
fine-tuning and evaluation, with an additional metastatic cancer dataset from
MIMIC III used to further assess reasoning consistency. GPT-4 demonstrated
superior interpretative capabilities, particularly in response to complex
prompts, yet displayed notable response-reasoning inconsistencies. In contrast,
open-source models like Falcon and LLaMA 2 achieved high accuracy but lacked
explanatory reasoning, underscoring the necessity for further research to
optimize both performance and interpretability. The study emphasizes the
significance of prompt engineering and the need for further exploration into
the unexpected reasoning-response misalignment observed in GPT-4. The results
underscore the promise of incorporating LLMs into healthcare diagnostics,
contingent upon methodological advancements to ensure accuracy and clinical
coherence of AI-generated outputs, thereby improving the trustworthiness of
LLMs for medical decision-making.

本研究评估了 GPT-3.5、GPT-4、Falcon 和 LLaMA 2 等最先进的大型语言模型在从出院摘要中识别患有轻度认知障碍（MCI）的患者的能力，并检查模型响应与其推理不一致的情况。研究结果强调了提示工程的重要性和对 GPT-4 中观察到的意外推理 - 响应不一致性进一步探索的需求，突显了将大型语言模型应用于医疗诊断的潜力，前提是在方法论上取得进步以确保人工智能生成的输出准确性和临床连贯性，从而提高大型语言模型在医疗决策中的可信度。

医学术语分类中的大型语言模型及响应与推理的意外不一致

Large Language Models in Medical Term Classification and Unexpected  Misalignment Between Response and Reasoning

Healthcare is one of the foremost applications of machine learning (ML).
Traditionally, ML models are trained by central servers, which aggregate data
from various distributed devices to forecast the results for newly generated
data. This is a major concern as models can access sensitive user information,
which raises privacy concerns. A federated learning (FL) approach can help
address this issue: A global model sends its copy to all clients who train
these copies, and the clients send the updates (weights) back to it. Over time,
the global model improves and becomes more accurate. Data privacy is protected
during training, as it is conducted locally on the clients' devices.
However, the global model is susceptible to data poisoning. We develop a
privacy-preserving FL technique for a skin cancer dataset and show that the
model is prone to data poisoning attacks. Ten clients train the model, but one
of them intentionally introduces flipped labels as an attack. This reduces the
accuracy of the global model. As the percentage of label flipping increases,
there is a noticeable decrease in accuracy. We use a stochastic gradient
descent optimization algorithm to find the most optimal accuracy for the model.
Although FL can protect user privacy for healthcare diagnostics, it is also
vulnerable to data poisoning, which must be addressed.

利用隐私保护联邦学习技术，发现健康诊断的机器学习模型会受到数据投毒的攻击，导致全局模型准确度下降。