mlsanityData to 14 Oct 2026

Hallucinations in medical devices

arXiv 2508.14118 ↗First posted 18 Aug 2025eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Computer methods in medical devices are frequently imperfect and are known to produce errors in clinical or diagnostic tasks. However, when deep learning and data-based approaches yield output that exhibit errors, the devices are frequently said to hallucinate. Drawing from theoretical developments and empirical studies in multiple medical device areas, we introduce a practical and universal definition that denotes hallucinations as a type of error that is plausible and can be either impactful or benign to the task at hand. The definition aims at facilitating the evaluation of medical devices that suffer from hallucinations across product areas. Using examples from imaging and non-imaging applications, we explore how the proposed definition relates to evaluation methodologies and discuss existing approaches for minimizing the prevalence of hallucinations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 75 of its 141 references that are in the map. First posted on arXiv 18 Aug 2025.

Builds on

75 of 141 references.

References · 75

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  7. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  8. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  10. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  11. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  12. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,729
38 more
  1. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,154
  2. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 763
  3. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMscited by 735
  4. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 719
  5. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  6. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 695
  7. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  8. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 518
  9. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 489
  10. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  11. 2024Measuring short-form factuality in large language modelscited by 373
  12. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  13. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  14. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 310
  15. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 291
  16. 2023Towards Generalist Biomedical AIcited by 289
  17. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 287
  18. 2024Knowledge Conflicts for LLMs: A Surveycited by 283
  19. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 274
  20. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 252
  21. 2023Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaborationcited by 251
  22. 2021Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Modelscited by 232
  23. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  24. 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 212
  25. 2023On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspectivecited by 211
  26. 2023Mitigating the Alignment Tax of RLHFcited by 183
  27. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 162
  28. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 157
  29. 2019Challenges in Building Intelligent Open-domain Dialog Systemscited by 147
  30. 2024Language Models with Conformal Factuality Guaranteescited by 131
  31. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 125
  32. 2023Investigating the Catastrophic Forgetting in Multimodal Large Language Modelscited by 119
  33. 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
  34. 2024Large language model validity via enhanced conformal prediction methodscited by 92
  35. 2017A Review of Convolutional Neural Networks for Inverse Problems in Imagingcited by 81
  36. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  37. 2024Towards Conversational Diagnostic AIcited by 79
  38. 2022Diffusion Probabilistic Models beat GANs on Medical Imagescited by 77

50 most cited of 75 All 75 (JSON)

Built on by

Not cited yet.