In brief Explainable artificial intelligence (XAI) is the process of making the outcome of non-linearly programmed systems more transparent, in an effort to avoid “black-box” processes. XAI in Computer Science is defined by tools and frameworks to help in understanding and interpret predictions made by the various machine learning models. Libraries The most commonly used…
Parameters main(q, d, m): The primary function that takes in the Question (q), Domain (d), and Medical (m) parameters and returns the Answer (a) and Grade (grade). • Decision Logic: Implements the conditional logic as per the pseudocode to determine the appropriate answer a. • answer_content(): A placeholder function to generate the answer content c.…
Abstract In this paper, we present a novel Artificial Intelligence (AI) -empowered system that enhances large language models and other machine learning tools with rules to provide primary care diagnostic advice to patients. Specifically, we introduce a novel methodology, represented through a process diagram, which allows the definition of generative AI processes and functions with…
Implementation A generalized python code to be used for classification tasks. Sklearn, tensorflow and keras are the main libraries used. For inference examples use the following github link Usage • Data Loading: You can load data from any SQLite database or CSV file by specifying the path and, if necessary, the SQL query. • Preprocessing…
Abstract Large Language Models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence (AI) technology which is rapidly evolving and promises to aid in medical diagnosis either by assisting doctors or by simulating a doctor’s workflow in more advanced and complex implementations. In this technical paper, we outline Cognitive Network Evaluation Toolkit for Medical Domains (COGNET-MD), which…