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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Global Health care Professionals' Perceptions of Large Language Model Use In Practice: Cross-Sectional Survey Study.

Ecem Ozkan¹, Aysun Tekin², Mahmut Can Ozkan¹

¹Department of Medicine, Jersey Shore University Medical Center, 1945 NJ-33, Neptune, NJ, 07753, United States, 1 5078843064.

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Summary

This summary is machine-generated.

Healthcare professionals widely use ChatGPT for various tasks, finding it beneficial for writing, research, and patient communication. However, concerns about accuracy, privacy, and ethical issues need systematic resolution.

Keywords:

AI ChatGPT LLM artificial intelligence chatbot clinical diagnostic accuracy educational educators efficiency global health care professionals language model large language model nurses patient care patient communication patient outcome physicians survey treatment planning utilization

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Area of Science:

Artificial Intelligence in Medicine
Natural Language Processing
Healthcare Technology Adoption

Background:

ChatGPT, a large language model, presents potential benefits for healthcare, including improved diagnostics, treatment planning, and patient outcomes.
Understanding healthcare professionals' perceptions of AI tools like ChatGPT is crucial for their effective integration into medical practice.

Purpose of the Study:

To evaluate healthcare professionals' awareness and perceptions of ChatGPT's applications in medicine.
To identify the perceived benefits and challenges associated with adopting ChatGPT in the medical field.

Main Methods:

A 33-question online survey was distributed to healthcare professionals globally.
The survey assessed demographics, familiarity with ChatGPT, perceived usefulness, reliability, and impact on patient care, research, and education.

Main Results:

115 healthcare professionals responded; 87.8% had heard of ChatGPT and 76.2% had used it.
ChatGPT was found useful for manuscript/email writing, accessing research, diagnosis suggestions, and patient communication.
Concerns included response accuracy (29.8%), limited applicability (38.3%), and legal/ethical issues (12.8%). Data encryption and access control were suggested for privacy.

Conclusions:

ChatGPT usage is prevalent among healthcare professionals for clinical, research, and educational activities.
While perceived as useful, significant concerns regarding accuracy, privacy, data security, and ethics require systematic attention.
Further research is needed to ascertain the impact of ChatGPT and similar AI on healthcare outcomes, addressing identified concerns through appropriate methodologies.