mlsanityData to 7 Oct 2026

The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?

arXiv 2012.06000 ↗First posted 10 Dec 2020cs.AIcited by 16

Not on the map. Why?

Abstract (arXiv)

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in textitA Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article will take readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability of medical AI.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 3; 2025: 6; 2026: 2.

16 papers.

Sort:

By area:

  1. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  2. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  3. 2024Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnosticscited by 11
  4. 2024SimSUM: Simulated Benchmark with Structured and Unstructured Medical Recordscited by 6
  5. 2021CapillaryNet: An Automated System to Quantify Skin Capillary Density and Red Blood Cell Velocity from Handheld Vital Microscopycited by 2
  6. 2023A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactualscited by 1
  7. 2022Learning Generative Factors of EEG Data with Variational auto-encoderscited by 0
  8. 2024Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imagingcited by 0
  9. 2025Trustworthy Tree-based Machine Learning by MoS2 Flash-based Analog CAM with Inherent Soft Boundariescited by 0
  10. 2025Uncertainty-Aware Learning Policy for Reliable Pulmonary Nodule Detection on Chest X-Raycited by 0
  11. 2025CLMN: Concept based Language Models via Neural Symbolic Reasoningcited by 0
  12. 2025Patient-level Information Extraction by Consistent Integration of Textual and Tabular Evidence with Bayesian Networkscited by 0
4 more
  1. 2025UniCoMTE: A Universal Counterfactual Framework for Explaining Time-Series Classifiers on ECG Datacited by 0
  2. 2025Medication counseling with large language models: balancing flexibility and rigiditycited by 0
  3. 2026Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classificationcited by 0
  4. 2026Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessmentcited by 0