Artificial Intelligence and ChatGPT Models in Healthcare

Abstract

The aim of this research is twofold: 1) to explore the potential applications of artificial intelligence (AI) and generative pre-trained transformer (GPT) models in healthcare and, 2) to identify the challenges associated with integrating these technologies into clinical practice. AI and GPT models have attracted significant attention within the healthcare industry due to their potential to revolutionize medical practices. Potential applications of AI and GPT models in healthcare include early disease detection through the analysis of medical images or electronic health records, personalized treatment recommendations based on patient data analysis, and improved efficiency through automating routine administrative tasks. These models employ advanced deep-learning algorithms to analyze extensive volumes of patient data, interpret medical images, and provide diagnostic suggestions. As a result, healthcare professionals can make well-informed decisions and enhance patient outcomes. In addition, AI and GPT models support remote monitoring, personalized care, and patient triaging, thereby improving the accessibility and efficiency of healthcare services. Nevertheless, the widespread adoption of AI and GPT models into healthcare faces several challenges and limitations. These models require high-quality data and must address issues related to data privacy, biased algorithms, and regulatory frameworks. Moreover, ethical considerations, including safeguarding patient privacy, ensuring algorithmic accountability, and avoiding biases, must be diligently addressed when implementing AI and GPT models within healthcare settings. This study is of AI and GPT models as they relate to healthcare, with the goal of encouraging future developments in this field.

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