Syed Sibtain, Ahmed Rehan, Iqbal Arshad, Ahmad Naveed, Alshara Mohammed Ali
School of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology (PAF-IAST), Mang, Haripur 22621, Khyber Pakhtunkhwa, Pakistan.
Sino-Pak Center for Artificial Intelligence (SPCAI), Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Mang, Haripur 22621, Khyber Pakhtunkhwa, Pakistan.
J Imaging. 2024 Dec 13;10(12):322. doi: 10.3390/jimaging10120322.
With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes.
随着技术进步，健康科学与人工智能（AI）的融合取得了显著进展。现代健康系统旨在简化患者诊断。然而，挑战在于为患者和医生提供基于人工智能的预防措施，以进行更准确的风险评估。所提出的医疗保健系统旨在将患者、医生、实验室、药房和行政人员的用例及其主要功能整合到一个单一平台上。所提出的框架还可以处理微观图像、CT扫描、X射线和MRI，以对恶性肿瘤进行分类，并为医生提供一组用于患者风险评估的人工智能预防措施。所提出的框架纳入了各种深度卷积神经网络（DCNN）模型，用于识别人体不同部位（即大脑、骨骼、肺部、肾脏和皮肤）的不同形式的肿瘤和骨折，并借助微调大语言模型（LLM）（即生成式预训练变换器4（GPT-4））生成预防措施。有了足够的训练数据，DCNN可以学习高度具有代表性、数据驱动的分层图像特征。由于GPT-4模型在先前的医学评估和研究中的解释、推理、记忆和准确性，因此选择它来生成预防措施。分类模型通过分类报告（即召回率、精确率、F1分数、支持度、准确率以及宏平均和加权平均）和混淆矩阵进行评估，并且与传统方案相比表现出强大的性能。
