mlsanityData to 7 Oct 2026

Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI

arXiv 2311.01463 ↗First posted 26 Sep 2023cs.CL cs.AI cs.CVcited by 33

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Abstract (arXiv)

Large language models have proliferated across multiple domains in as short period of time. There is however hesitation in the medical and healthcare domain towards their adoption because of issues like factuality, coherence, and hallucinations. Give the high stakes nature of healthcare, many researchers have even cautioned against its usage until these issues are resolved. The key to the implementation and deployment of LLMs in healthcare is to make these models trustworthy, transparent (as much possible) and explainable. In this paper we describe the key elements in creating reliable, trustworthy, and unbiased models as a necessary condition for their adoption in healthcare. Specifically we focus on the quantification, validation, and mitigation of hallucinations in the context in healthcare. Lastly, we discuss how the future of LLMs in healthcare may look like.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

17 of 42 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  6. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  7. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  8. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 457
  9. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  10. 2023Certifying LLM Safety against Adversarial Promptingcited by 248
  11. 2022Factuality Enhanced Language Models for Open-Ended Text Generationcited by 200
  12. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
5 more
  1. 2023Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Datasetcited by 90
  2. 2023Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinationscited by 63
  3. 2022An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Taskscited by 36
  4. 2022Foresight -- Generative Pretrained Transformer (GPT) for Modelling of Patient Timelines using EHRscited by 11
  5. 2023Challenges in Domain-Specific Abstractive Summarization and How to Overcome themcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 17; 2025: 10; 2026: 6.

33 papers.

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  1. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 109
  2. 2024A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Modelscited by 98
  3. 2024DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucinationcited by 47
  4. 2024JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capabilitycited by 32
  5. 2024Exploring Autonomous Agents through the Lens of Large Language Models: A Reviewcited by 30
  6. 2025A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcarecited by 27
  7. 2024Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarizationcited by 23
  8. 2024Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generationcited by 23
  9. 2024DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinationscited by 23
  10. 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Datacited by 21
  11. 2024How well do LLMs cite relevant medical references? An evaluation framework and analysescited by 20
  12. 2025Automatic Evaluation of Healthcare LLMs Beyond Question-Answeringcited by 19
21 more
  1. 2024Correctness is not Faithfulness in RAG Attributionscited by 18
  2. 2024A Survey on Trustworthiness in Foundation Models for Medical Image Analysiscited by 16
  3. 2024Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Modelscited by 15
  4. 2024Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attackscited by 14
  5. 2025MedCite: Can Language Models Generate Verifiable Text for Medicine?cited by 11
  6. 2024ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Modelscited by 9
  7. 2025Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalitiescited by 6
  8. 2025A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysiscited by 5
  9. 2024Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerationscited by 4
  10. 2025Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilizationcited by 4
  11. 2025SlideBot: A Multi-Agent Framework for Generating Informative, Reliable, Multi-Modal Presentationscited by 4
  12. 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
  13. 2025Trustworthy Agents for Electronic Health Records through Confidence Estimationcited by 2
  14. 2025Increasing LLM response trustworthiness using voting ensemblescited by 1
  15. 2026A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbotscited by 1
  16. 2026Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Studycited by 1
  17. 2024Effects of Different Prompts on the Quality of GPT-4 Responses to Dementia Care Questionscited by 0
  18. 2026Effects of Cross-lingual Evidence in Multilingual Medical Question Answeringcited by 0
  19. 2026Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Modelscited by 0
  20. 2026How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generationcited by 0
  21. 2026Evaluating AI Generated Summaries for Cancer Patientscited by 0