ChatGPT: how to use it and the pitfalls/cautions in academia

The integration of large language models (LLMs) in academic research has transformed traditional research methodologies. This review investigates the current state, applications, and limitations of LLMs, particularly ChatGPT, in medical and scientific research. I performed a systematic review of recent literature and LLM development reports in artificial intelligence-assisted research tools, including commercial LLM services (GPT-4o, Claude 3, Gemini Pro) and specialized research platforms (Genspark, Scispace). I evaluated their performance, applications, and limitations across stages of the research process. Recent advancements in LLMs shows potential for improving research efficiency, particularly in literature review, data analysis, and manuscript preparation. Performance comparison revealed varying strengths: GPT-4o and o1 outperformed in the overall area, Claude 3 in writing and coding, and Gemini Pro in multimodal processing. Therefore, it is important to choose and use each model wisely according to its advantages. However, hallucination risks, inherent biases, plagiarism concerns, and privacy issues are concerns in LLMs. The emergence of Retrieval-Augmented Generation models and specialized research tools has improved accuracy and current information access. LLMs offer effective support for research productivity, but they should serve as complementary tools rather than primary research drivers. The successful application of these tools depends on a thorough understanding of their limitations, strict adherence to ethical guidelines, and preservation of researcher autonomy.