{
  "id": 5383611,
  "title": "Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction",
  "url": "https://urgent.news/2026/09/03/evaluating-large-language-models-as-tools-to-navigate-researchers-in",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-09-03T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748827v1?rss=1"
  },
  "original_language": "en",
  "account": "Large Language Models (LLMs) have shown promise as valuable tools to help researchers streamline literature reviews and accelerate the synthesis of academic knowledge. These models, however, raise concerns about reliability due to potential errors and fabrications, known as hallucinations. The critical question is whether LLMs can consistently generate thorough, current literature surveys and analyses. To address this, this study examined the performance of three leading LLMs – OpenAI's ChatGPT, Google's Gemini, and DeepSeek – on the task of creating a comprehensive survey paper on deep learning applications for predicting cancer drug responses (DRP). The researchers tested both standard and specialized \"Deep Research\" (DR) or \"Deep Think\" (DT) modes with prompts of different levels of detail. The evaluation focused on key academic aspects such as reference management, content quality, and analytical depth.\n\nThe findings indicate that DR modes significantly enhance reliability by preventing hallucinations, but performance differences persist between the models and across various prompts. A trade-off between the number of references and how well they are integrated into the output was observed. Even the best-performing models did not match the analytical sophistication of human experts and often necessitated extensive human oversight. The study concludes that while LLMs currently function as potent assistive tools, they cannot entirely replace the critical validation and synthesis capabilities of human researchers. The choice of LLM depends on the specific task, and several strategies can be employed to optimize the generated output.",
  "summary": "Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}