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 chapter, we outline Cognitive Network Evaluation Toolkit for Medical Domains (COGNET-MD), which constitutes a novel benchmark for LLM evaluation in the medical domain. Specifically, we propose a scoring-framework with increased difficulty to assess the ability of LLMs (Panagoulias et al. in COGNET-MD, an evaluation framework and dataset for large language model benchmarks in the medical domain, 2024; Panagoulias et al. in Dermacen analytica: a novel methodology integrating multi-modal large language models with machine learning in tele-dermatology, 2024; Panagoulias et al. in, Evaluating LLM—generated multimodal diagnosis from medical images and symptom analysis, 2024) in interpreting medical text. The proposed framework is accompanied with a database of Multiple Choice Quizzes (MCQs). To ensure alignment with current medical trends and enhance safety, usefulness, and applicability, these MCQs have been constructed in collaboration with several associated medical experts in various medical domains and are characterized by varying degrees of difficulty. The current (first) version of the database includes the medical domains of Psychiatry, Dentistry, Pulmonology, Dermatology and Endocrinology, but it will be continuously extended and expanded to include additional medical domains.