Abstract: This research explores the integration of GPT-based language models in the healthcare sector, focusing on Adaptive Intelligence. It delves into the transformative possibilities and profound implications of incorporating these models into critical healthcare domains, such as clinical decision-making, medical imaging, and personalized medicine. Demonstrating remarkable adaptability, these models offer innovative solutions to dynamic medical challenges. However, adopting adaptive intelligence requires careful consideration of ethical boundaries, including patient data privacy, transparency, and legal compliance. The outlined strategies encompass dynamic adaptation, cross-domain knowledge transfer, and robust validation processes, laying the foundation for deploying GPT-based models in diverse healthcare settings. Looking forward, imminent advancements in medical research and shifts in clinical practice demand solid policy frameworks to address emerging challenges. Collaboration among ethicists, clinicians, data scientists, and policymakers is paramount to establishing guidelines ensuring the appropriate and responsible use of adaptive science. As the healthcare landscape evolves, the research emphasizes the critical role of interdisciplinary collaboration in unlocking the full potential of GPT, promising advancements in patient care and healthcare delivery. The study anticipates a future marked by transformative changes in medical research paradigms and underscores the need for comprehensive policy frameworks to navigate forthcoming challenges.
Keywords: Adaptive Intelligence, GPT models, Large Language Models, Healthcare Integration, collaboration.
Cite:
Karthik Meduri, Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pramod Pawar, Deepak Kumar,"Adaptive Intelligence: GPT-Powered Language Models for Dynamic Responses to Emerging Healthcare Challenges", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13114.
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DOI:
10.17148/IJARCCE.2024.13114
