mlsanityData to 7 Oct 2026

Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician

arXiv 2107.04540 ↗First posted 9 Jul 2021physics.med-ph eess.IVcited by 15

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI)-based methods are showing promise in multiple medical-imaging applications. Thus, there is substantial interest in clinical translation of these methods, requiring in turn, that they be evaluated rigorously. In this paper, our goal is to lay out a framework for objective task-based evaluation of AI methods. We will also provide a list of tools available in the literature to conduct this evaluation. Further, we outline the important role of physicians in conducting these evaluation studies. The examples in this paper will be proposed in the context of PET with a focus on neural-network-based methods. However, the framework is also applicable to evaluate other medical-imaging modalities and other types of AI methods.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 4; 2024: 1; 2025: 3; 2026: 2.

15 papers.

Sort:

  1. 2025Metrics that matter: Evaluating image quality metrics for medical image generationcited by 12
  2. 2022Assessing the ability of generative adversarial networks to learn canonical medical image statisticscited by 11
  3. 2023A task-specific deep-learning-based denoising approach for myocardial perfusion SPECTcited by 4
  4. 2022Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterizationcited by 3
  5. 2023Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECTcited by 3
  6. 2022Prior image-based medical image reconstruction using a style-based generative adversarial networkcited by 2
  7. 2022Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imagingcited by 1
  8. 2023DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECTcited by 1
  9. 2024Nuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024)cited by 1
  10. 2025What is Implementation Science; and Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imagingcited by 1
  11. 2022Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)cited by 0
  12. 2023A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapycited by 0
3 more
  1. 2025Objective Task-based Evaluation of Quantitative Medical Imaging Methods: Emerging Frameworks and Future Directionscited by 0
  2. 2026Benchmarking Deep Learning-Based Reconstruction Methods for Photoacoustic Computed Tomography with Clinically Relevant Synthetic Datasetscited by 0
  3. 2026Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframingcited by 0