The development of generative AI technology has brought new prospects in several industries, including healthcare. This paper presents a case study assessing how well Microsoft Copilot, Google Gemini, OpenAI ChatGPT, and Anthropic Claude, four most popular AI systems, help with cardiology diagnosis. Analyzing Holter monitor imaging data from six patient cases, we explore the accuracy and practicality of these AI models. Our approach emphasizes the need of crafting prompts, while following ethical principles and European privacy regulations, for the production of precise and thorough AI responses. All four AI systems are shown to perform well in the study, with Google Gemini being correct most of the time. Microsoft Copilot delivers reliable results, however Claude and GPT-4o tend to overestimate patient conditions even when they identify critical parameters like drug adherence and dosage efficacy. For a three-month patient assessment, GPT-4o showed competence. This work demonstrates how generative AI might improve diagnostic procedures and seeks to add to the conversation on AI's application in patient care.