Revolutionizing Healthcare With Large Language Models: Advancements, Challenges, and Future Prospects in Ai-driven Diagnostics and Decision Support
Year: 2025
Type: article
Abstract: Large Language Models (LLMs) such as BERT, GPT-3, and GPT-4 play a central role in revolutionizing diagnostic support and healthcare decision-making. These premium AI solutions essentially boost machine clinical analysis through vast datasets such as electronic health records (EHRs), medical literature, imaging data, and genomic data. Deep learning and NLP features in LLMs allow for disease detection at an early stage, treatment plans as per the patient as well as monitoring of the patient; all these make clinical procedures easier. However, virtual assistants powered by AI as well as chatbots keep patients interactive while easing the burden of healthcare professionals. Of course, with all the benefits LLMs have contributed to the world of today-processing much work in bulk at fast speed-globally they raise critical challenges such as data security concerns bias in AI system predictions and transparency in decision-making. Ethical use of AI in healthcare demands adherence to legislation like HIPAA and GDPR to protect data with principles based on fairness accountability and interpretability. IMMultimodal integration of textual visual genetic data will be more precise but requires federated learning transfer learning and data augmentation such that models are enhanced without compromising privacy. Cooperative effort among AI researchers, physicians who give input on policy development towards developing trusted transparent fair standards-based diagnostic tools with the help of ML in medicine is necessary. The present study explores how LLMs have evolved over time in the healthcare industry in terms of challenges encountered as well as probable outcomes integrated into democratizing medical information along with progress in precision medicine.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7083588581
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