Can AI generate abstracts of scientific research papers based on topics?

AI can generate scientific research paper abstracts based on given topics. This application of artificial intelligence, primarily using Natural Language Processing (NLP) and large language models (LLMs), is technically feasible and increasingly common. The effectiveness depends on factors like the model's training data quality and specificity, the clarity and detail of the input topic keywords or descriptions, and the complexity of the target research domain. Models fine-tuned on scholarly literature generate more coherent and relevant abstracts than general-purpose models. While they can synthesize plausible summaries, outputs require careful validation for factual accuracy, originality, and alignment with the specific research contributions, as AI may hallucinate details or miss critical nuances. AI-generated abstracts serve as valuable tools for accelerating literature reviews, aiding researchers during the initial drafting phase, and enhancing discovery of relevant literature. However, they function best as drafting aids requiring rigorous human oversight and revision to ensure scientific integrity and precision before formal use.