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Navigating the AI Ecosystem: A Practical Guide to Leveraging Large Language Models and Their Platform-Specific Features for Medical Research and Education
ABSTRACT
Large language models (LLMs) have rapidly evolved from general-purpose conversational tools into sophisticated, feature-rich platforms with distinct capabilities specifically relevant to medical research and education. While existing literature has evaluated LLM performance on standardized clinical benchmarks, no practical guidance currently exists to help clinicians, researchers, and educators navigate the heterogeneous ecosystem of platform-specific features now available across leading systems including ChatGPT, Claude, Google Gemini, Grok, Perplexity AI. This tutorial addresses that gap by providing a structured, task-oriented guide to seven major feature categories. A platform selection decision framework mapping specific research and educational tasks to optimal platform features is presented as a practical reference tool. This tutorial demonstrates that strategic, feature-aware deployment of LLM platforms rather than reliance on a single general-purpose model offers substantially greater research productivity and educational utility, and that the alignment of platform feature to task requirement is the primary determinant of effective AI-assisted research and education outcomes in healthcare.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.