mlsanityData to 7 Oct 2026

Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness

arXiv 2311.11211 ↗First posted 19 Nov 2023cs.AIcited by 3

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Abstract (arXiv)

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 33 of its 71 references that are in the map. First posted on arXiv 19 Nov 2023.

Builds on

33 of 71 references.

References · 33

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21 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcarecited by 27
  2. 2024Closing the gap between open-source and commercial large language models for medical evidence summarizationcited by 21
  3. 2026BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discoverycited by 1