NLP AI Models for Optimizing Medical Research: Demystifying the Concerns

Natural language processing (NLP) AI models have gained popularity in research; however, ethical considerations are necessary to avoid potential negative consequences. This paper identifies and explores the key areas of ethical concern for researchers using NLP AI models, such as bias in training data and algorithms, plagiarism, data privacy, accuracy of generated content, prompt and content generation, and training data quality. To mitigate bias, researchers should use diverse training data and regularly evaluate models for potential biases. Proper attribution and privacy protection are essential when using AI-generated content, while accuracy should be regularly tested and evaluated. Specific and appropriate prompts, algorithms, and techniques should be used for content generation, and training data quality should be high, diverse, and updated regularly. Finally, appropriate authorship credit and avoidance of conflicts of interest must be ensured. Adherence to ethical standards, such as those outlined by ICMJE, is crucial. These ethical considerations are vital for ensuring the quality and integrity of NLP AI model research and avoiding negative consequences.