mlsanityData to 13 Oct 2026

Trust and Medical AI: The challenges we face and the expertise needed to overcome them

arXiv 2008.07734 ↗First posted 18 Aug 2020cs.AI cs.CYcited by 21

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Abstract (arXiv)

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. However, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes two contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.

Key facts. Cited by 21 papers in the map (data through 13 Oct 2026). First posted on arXiv 18 Aug 2020.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 3; 2025: 10; 2026: 2.

21 papers.

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  1. 2025Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generationcited by 23
  2. 2025TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviewscited by 9
  3. 2025MedBench v4: A Robust and Scalable Benchmark for Evaluating Chinese Medical Language Models, Multimodal Models, and Intelligent Agentscited by 8
  4. 2023May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainabilitycited by 5
  5. 2023Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understandingcited by 4
  6. 2021Readying Medical Students for Medical AI: The Need to Embed AI Ethics Educationcited by 3
  7. 2023Utilizing Segment Anything Model For Assessing Localization of GRAD-CAM in Medical Imagingcited by 3
  8. 2025HealthQA-BR: A System-Wide Benchmark Reveals Critical Knowledge Gaps in Large Language Modelscited by 3
  9. 2024Rapid Review of Generative AI in Smart Medical Applicationscited by 2
  10. 2024Enhancing Radiological Diagnosis: A Collaborative Approach Integrating AI and Human Expertise for Visual Miss Correctioncited by 2
  11. 2025Insights into a radiology-specialised multimodal large language model with sparse autoencoderscited by 2
  12. 2024MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Modelscited by 1
9 more
  1. 2025Position: Restructuring of Categories and Implementation of Guidelines Essential for VLM Adoption in Healthcarecited by 1
  2. 2025RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcutscited by 1
  3. 2022When Performance is not Enough -- A Multidisciplinary View on Clinical Decision Supportcited by 0
  4. 2023Search-Based Fairness Testing: An Overviewcited by 0
  5. 2025Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validationcited by 0
  6. 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0
  7. 2025Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platformcited by 0
  8. 2026GraphFlow: An Architecture for Formally Verifiable Visual Workflows Enabling Reliable Agentic AI Automationcited by 0
  9. 2026BioMed-Agent-RL: A Meta Learning, All You Need for Biomedical Applicationscited by 0