Description
In recent times, the domain of artificial intelligence (AI) has demonstrated substantial progress across diverse sectors, including healthcare. This advancement extends to the emergence of conversational AI models such as ChatGPT, a creation of OpenAI. These models are adept at engaging in natural language conversations, furnishing information, offering assistance, and even aiding in decision-making processes. Within the healthcare realm, ChatGPT holds the potential to revolutionize patient support, medical consultations, and various other facets of the healthcare landscape. The present article explores the ramifications of ChatGPT’s integration in healthcare support and the associated implications.
The transformative influence of AI on healthcare has been a subject of investigation by researchers and scholars. Notably, ChatGPT’s introduction aligns with the trajectory of AI integration in medicine. Rajkomar, Dean, and Kohane (2019) highlighted the role of machine learning in medicine, outlining its potential impact on diagnostics and treatment. Topol (2019) emphasized the convergence of human and artificial intelligence, heralding the era of high-performance medicine. Similarly, Wang, Casalino, and Khullar (2019) underscored the application of deep learning in radiology, accentuating its potential in medical imaging analysis. Meanwhile, Miotto et al. (2017) offered a comprehensive overview of deep learning’s implications and challenges in healthcare, emphasizing its relevance in clinical decision-making. These studies collectively underscore the profound potential of AI-driven technologies in healthcare transformation.
The integration of AI technologies, exemplified by ChatGPT, resonates with the broader discourse on transparency and ethical considerations in AI (Beam & Kohane, 2016). This pertains to the need for clear communication of AI’s decision-making processes, a crucial aspect in healthcare where decisions impact patient well-being. ChatGPT’s capacity to engage in nuanced conversations intersects with the concept of subtyping in precision medicine (Saria & Goldenberg, 2017), which seeks to categorize patient populations for targeted interventions.
Furthermore, the concept of digital communication in healthcare, as discussed by Car and Sheikh (2004), elucidates the acceptance and secure utilization of technology-mediated consultations. Halamka (2011) has also envisioned the potential of health information exchange to revamp healthcare delivery.
The Rise of Conversational AI in Healthcare
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