Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper argues that trustworthy AI in medical imaging requires ethical checks built into each of five development stages, and it supplies stage-specific questions and data-access rules for doing so.

desk verdict A usable five-stage ethics checklist for medical imaging AI, but the framework contradicts itself on consent and validates itself; worth peer review after fixes. read the letter →

arxiv 2507.04249 v1 pith:UP74A2RZ submitted 2025-07-06 cs.CY cs.ET

Umer Sadiq Khan , Saif Ur Rehman Khan This is my paper · ORCID
classification cs.CYcs.ET
keywords artificialintelligenceethicsmedicalimagingclinicaldecisionsdataprivacyandsecurityAIlifecycleinformedconsentfairness
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that AI systems for medical imaging become trustworthy only if ethical scrutiny is built into each phase of development, not bolted on at the end. It organizes the lifecycle into five stages—data collection, data processing, model training, model evaluation, and deployment—and assigns each stage its own ethical emphasis, such as informed consent and anonymization at collection, bias mitigation at training, clinical relevance at evaluation, and continuous monitoring at deployment. The paper's core deliverable is a practical toolkit: a table of stage-specific ethical questions and an accessibility matrix that tells public, private, and third-party stakeholders what data access is ethically permissible at each stage. The paper frames this as filling a gap left by existing ethics discussions, which address individual issues but not the whole lifecycle as a design problem.

What carries the argument

The central object is a five-stage AI lifecycle—data collection, data processing, model training, model evaluation, and deployment—treated as a single unit of ethical design. Two tables carry the argument: Table 1 ('Ethics accessibility level') defines who may access data at each stage, and Table 2 ('Ethical quality check') turns each stage's governing principle into a concrete question a developer must answer. Figure 3's 'interrelated integrated ethics' diagram ties the stages together and supplies the rationale for why some principles are marked non-applicable at certain stages, keeping the checklist from spreading attention too thin.

What would settle it

Check the consent forms of deployed medical-imaging AI systems: if a majority lack explicit clauses authorizing AI training, evaluation, and deployment, the framework's non-applicability rationale fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the five core principles—data privacy, fairness, transparency, accountability, and autonomy—do not all apply with equal weight at every stage, and that a trustworthy medical-imaging AI depends on matching each principle to the stage where it matters most. It maps data collection to consent, anonymization, and diversity; data processing to security, bias detection, and documentation; model training to fairness and accountability; model evaluation to transparency, clinical relevance, and thorough testing across demographics; and deployment to continuous monitoring, risk assessment, and regulatory compliance. To make this mapping usable, it proposes an 'ethical quality check' table of questions for each stage and a data-accessibility table that keeps collection and deployment restricted while making evaluation comparatively open. It also gives explicit reasons for marking some principles non-applicable at certain stages, most notably treating consent and autonomy as settled after data collection so they do not recur during training, evaluation, or deployment.

Load-bearing premise

The framework assumes that consent obtained when data is collected legally covers later model training, evaluation, and clinical deployment, so it drops consent and autonomy checks from those three stages.

Editorial extensions

If this is right

  • A team following the framework would conduct explicit ethics checks at each of the five stages instead of relying on one approval gate before clinical use.
  • Data collection and deployment would be the most tightly restricted phases, with third parties barred or limited, while model evaluation would be deliberately open to external validation.
  • Consent forms would need to be drafted at collection time to cover future AI training, evaluation, and deployment, because the framework assumes no further consent is required at those stages.
  • Continuous post-deployment monitoring would become an ethical duty, covering emerging bias, performance drift, and communication of model updates to clinicians and patients.
  • Legal and technical standards such as HIPAA, GDPR, DICOM, and ISO would function as built-in checkpoints inside the data handling stages rather than external afterthoughts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the stage-by-stage questions could be encoded as a machine-readable audit checklist or traceability matrix, making 'ethics by design' testable in software reviews and regulatory filings.
  • Editorial inference: the accessibility matrix's labels (restricted, limited, open) could be checked against real data-sharing agreements and legal rulings to see whether they match actual practice.
  • Editorial inference: the decision to treat consent as settled after data collection is the framework's most fragile point; a regulatory change requiring fresh consent for each new AI use would force rework of training, evaluation, and deployment stages.
  • Editorial inference: running the checklist against documented AI imaging failures, such as biased triage or diagnostic tools, would test whether each failure maps to a stage the framework would have caught.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper proposes a lifecycle framework for integrating ethics into the development of AI systems for medical imaging. It structures the discussion around five stages (data collection, data processing, model training, model evaluation, deployment) and introduces two main tools: Table I, which assigns ethics accessibility levels to public, private, and third-party entities, and Table II, a set of stage-specific ethical quality checks. The central claim is that systematically applying these stage-specific considerations, as represented by the tables, supports the creation of ethically sound AI systems that respect patient privacy, fairness, transparency, accountability, and autonomy. The manuscript is a narrative review; it does not present empirical validation or a formal evaluation of the proposed framework.

Significance. If the framework were internally consistent and properly validated, it could serve as a practical checklist for AI developers and healthcare organizations. The topic is timely, and the paper correctly emphasizes that ethical issues must be addressed at each stage of the AI lifecycle rather than only at the point of data collection. The use of references to regulations such as GDPR and HIPAA is appropriate, and the stage-by-stage breakdown is a useful organizing principle. However, the paper's contribution is limited by a major internal contradiction regarding consent, the absence of any external validation or comparison with existing frameworks, and a lack of methodological detail in the review process. These issues prevent the paper from supporting its strong claims about 'ethically sound AI' in its current form.

major comments (3)
  1. [§4.6.3, §2.3, Table II] There is a direct and load-bearing contradiction in the treatment of informed consent and autonomy. Section 2.3 states that "Informed consent is paramount" and that participants must have comprehensive knowledge of data use "especially in contexts they may not anticipate, such as AI training." Section 4.6.3, however, declares consent and autonomy "not applicable" during model training, model evaluation, and deployment, asserting that initial consent obtained at data collection is presumed to cover these activities. Table II independently includes an informed-consent check under Model Training ("Is consent obtained where necessary?"). This inconsistency removes autonomy-related checks from three of the five stages, directly undermining the paper's claim to protect patient autonomy at every stage. The presumption that initial consent automatically covers downstream AI training and deployment is also legally and ethically unsupported; regulations such as GDPR and the cited literature (e.g., ref. [4]) generally do not allow clinical consent to cover unanticipated AI uses without additional safeguards. The authors should reconcile these positions, either by requiring re-consent or a documented justification for new uses at each stage, or by explicitly revising Section 2.3 and Table II to limit the consent requirement.
  2. [§5 (Table II) and §7 (Conclusion)] The paper presents Table II as a set of ethical probes that ensure "trustworthy AI system development," and the conclusion asserts that the proposed inquiries support "the creation of ethically sound AI systems." However, the table is generated by the authors themselves, and no external benchmark, expert validation, case study, or comparison with established ethical guidelines is offered to show that these checks are comprehensive, effective, or sufficient. The framework is thus both the proposal and the evaluation instrument; its claimed comprehensiveness is circular. To make the central claim defensible, the authors should either add an evaluation protocol (for example, a survey of ethics experts, an application to a real-world case, or a mapping of Table II onto established frameworks such as the EU AI Act or WHO guidance) or substantially temper the language to present the table as a proposal requiring validation rather than a demonstrated guarantee.
  3. [Table I and §3.2] The accessibility levels in Table I ("No," "Yes," "Limited" for public, private, and third-party entities at each stage) are presented as though they are self-evident, but no justification, derivation, or citation is given for the assignments. For example, the table states that data processing is "Yes" for public access while data collection is "No," and that model evaluation is "Yes" for all entities, but the text does not explain why these distinctions are ethically appropriate. Since Table I is used in Section 3.2 to argue that ethical considerations are directly integrated into data-access management, these assignments are load-bearing. The authors should either provide a rationale for each level, ground them in specific regulatory or ethical arguments, or clearly state that they are illustrative suggestions rather than normative requirements.
minor comments (5)
  1. [Throughout] The manuscript contains numerous typographical and grammatical errors that at times impede readability. Examples include "propped an ethical model" in Section 2, "through investigation" in Section 1.1 (likely "thorough investigation"), the duplicated phrase "virtual assistants" in the Introduction, and inconsistent use of "anonymous" versus "anonymized." A thorough language-editing pass is needed.
  2. [Section 6] Section 6, "Application of Software in Engineering: AI in Medical Imaging," appears disconnected from the rest of the paper and largely restates general material on AI in medical imaging without connecting it to the proposed ethics framework. It could be removed or integrated into the earlier sections to improve focus.
  3. [Methods] The paper describes itself as an analytical review but does not report a search strategy, inclusion criteria, or a method for synthesizing the literature. A brief methods paragraph explaining how the reviewed literature and guidelines were selected would improve transparency and reproducibility.
  4. [References] Some cited references are not listed in the reference list, and some listed references are not cited in the text. The authors should verify that all citations and reference entries are consistent and complete.
  5. [Table numbering] The tables are referred to inconsistently as "Table 1" and "Table I" (and similarly for Table 2/II). The notation should be standardized.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the lifecycle framework is a literature-based proposal, not a derivation; the §4.6.3 consent presumption is an unsupported assumption, not a circular reduction.

full rationale

The paper makes no predictive or first-principles derivation. Its central product is a qualitative ethical framework that maps recognized ethical principles onto five AI development stages, supported by literature review, regulations, and proposed checklists. Table II's ethical questions are presented as design proposals for future use, not as evidence that the framework is externally validated; the conclusion simply restates the proposal. There are no fitted parameters, no equations, no predictions that reduce to inputs, and no self-citations: the reference list contains no work by the authors. The closest concern is Section 4.6.3, where consent and autonomy are declared not applicable to model training, evaluation, and deployment because 'initial consents that were obtained during the data collection phase are presumed to cover these activities.' That presumption is unsupported and internally inconsistent with Section 2.3, which calls informed consent 'paramount' in AI-training contexts, and with Table II's Model Training informed-consent check. However, this is an argumentative gap or incompleteness in the framework, not a circular reduction of the framework's output to its own input. Under the stated scoring criteria, the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The framework rests on several unvalidated domain assumptions about how consent and ethical principles map to lifecycle stages. No free parameters or invented entities are present because the paper contains no quantitative model.

assumptions (3)
  • domain assumption Ethical principles can be allocated to lifecycle stages with some principles non-applicable at certain stages.
    Section 4.6 declares transparency, accountability, and consent as not applicable at particular stages, which is a modeling choice presented as fact.
  • domain assumption Informed consent obtained during data collection covers model training, evaluation, and deployment.
    Section 4.6.3 assumes initial consents cover later AI development activities.
  • ad hoc to paper The accessibility levels in Table I represent ethical requirements.
    The table is constructed by the authors without external validation or reference to policy sources.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment." pith.science (2026). https://pith.science/paper/UP74A2RZ

@misc{pith2026250704249,
  author       = {Pith},
  title        = {Pith review of: Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UP74A2RZ}},
  note         = {Machine review of arXiv:2507.04249}
}
read the original abstract

The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data collection to deployment. Addressing these concerns is essential for ensuring that AI systems are developed and implemented in a manner that respects patient rights and promotes fairness. This study aims to explore the ethical implications of AI in medical imaging, focusing on five key stages: data collection, data processing, model training, model evaluation, and deployment. The goal is to evaluate how these stages adhere to fundamental ethical principles, including data privacy, fairness, transparency, accountability, and autonomy. An analytical approach was employed to examine the ethical challenges associated with each stage of AI development. We reviewed existing literature, guidelines, and regulations concerning AI ethics in healthcare and identified critical ethical issues at each stage. The study outlines specific inquiries and principles for each phase of AI development. The findings highlight key ethical issues: ensuring patient consent and anonymization during data collection, addressing biases in model training, ensuring transparency and fairness during model evaluation, and the importance of continuous ethical assessments during deployment. The analysis also emphasizes the impact of accessibility issues on different stakeholders, including private, public, and third-party entities. The study concludes that ethical considerations must be systematically integrated into each stage of AI development in medical imaging. By adhering to these ethical principles, AI systems can be made more robust, transparent, and aligned with patient care and data control. We propose tailored ethical inquiries and strategies to support the creation of ethically sound AI systems in medical imaging.

Figures

Figures reproduced from arXiv: 2507.04249 by the authors.

Figure 1
Figure 1. Visual encapsulation analysis dissects ethics through the lens of five pivotal [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Ethics considerations for public access, private, and third-party access in AI [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Interrelated integrated ethics across the AI development in medical imaging. [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FOLC-Net: A Federated-Optimized Lightweight Architecture for Enhanced MRI Disease Diagnosis across Axial, Coronal, and Sagittal Views

    cs.CV 2025-07 reject novelty 3.0 of 10

    A federated, MRFO-optimized shallow CNN (FOLC-Net) reportedly beats heavier baselines on single-view brain tumor MRI classification, especially the sagittal view.

Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages · cited by 1 Pith paper

  1. [4]

    A. L. Kotsenas, P. Balthazar, D. Andrews, J. R. Geis, and T. S. Cook, ”Rethinking patient consent in the era of artificial intelligence and big data,” Journal of the American College of Radiology , vol. 18, no. 1, pp. 180-184, 2021

    work page 2021

  2. [1]

    J. Wu, W. Ji, H. Fu, M. Xu, Y. Jin, and Y. Xu, ”MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024, vol. 38, no. 6, pp. 6030-6038

    work page 2024

  3. [2]

    Christen, B

    M. Christen, B. Gordijn, and M. Loi, The ethics of cybersecurity. Springer Nature, 2020

    work page 2020

  4. [3]

    Rajasegar, P

    R. Rajasegar, P. Gouthaman, V. Ponnusamy, N. Arivazhagan, and V. Nallarasan, ”Data Privacy and Ethics in Data Analytics,” in Data Analytics and Machine Learning: Navigating the Big Data Landscape : Springer, 2024, pp. 195-213

    work page 2024

  5. [5]

    M. J. Willemink et al., ”Preparing medical imaging data for machine learning,” Radiology, vol. 295, no. 1, pp. 4-15, 2020. 23

    work page 2020

  6. [6]

    Padmapriya and S

    S. Padmapriya and S. Parthasarathy, ”Ethical data collection for med- ical image analysis: a structured approach,” Asian Bioethics Review , vol. 16, no. 1, pp. 95-108, 2024

    work page 2024

  7. [7]

    C.-W. Chang et al., ”Surface-based volumetric image reconstruction for image-guided procedures using a data-driven framework,” in Med- ical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 2024, vol. 12928: SPIE, pp. 466-471

    work page 2024

  8. [8]

    MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation

    J. Huang et al., ”MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation,” arXiv preprint arXiv :2402.18451, 2024

    work page Pith review arXiv 2024

Show all 57 references
  1. [9]

    Wu et al., ”Medsegdiff: Medical image segmentation with diffusion probabilistic model,” in Medical Imaging with Deep Learning , 2024: PMLR, pp

    J. Wu et al., ”Medsegdiff: Medical image segmentation with diffusion probabilistic model,” in Medical Imaging with Deep Learning , 2024: PMLR, pp. 1623-1639

    2024

  2. [10]

    B. M. Rashed and N. Popescu, ”Medical Image-Based Diagnosis Us- ing a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Op- timized by GA with a Deep Network Model for Features Extraction,” Mathematics, vol. 12, no. 5, p. 633, 2024

    2024

  3. [11]

    Alexander, A

    A. Alexander, A. Jiang, C. Ferreira, and D. Zurkiya, ”An intelligent future for medical imaging: a market outlook on artificial intelligence for medical imaging,” Journal of the American College of Radiology , vol. 17, no. 1, pp. 165-170, 2020

    2020

  4. [12]

    D. B. Larson, D. C. Magnus, M. P. Lungren, N. H. Shah, and C. P. Langlotz, ”Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework,” Radiology, vol. 295, no. 3, pp. 675-682, 2020

    2020

  5. [13]

    S. K. Zhou et al., ”A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress high- lights, and future promises,” Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, 2021

    2021

  6. [14]

    Ahmed, K

    Z. Ahmed, K. Mohamed, S. Zeeshan, and X. Dong, ”Artificial intel- ligence with multi-functional machine learning platform development for better healthcare and precision medicine,” Database, vol. 2020, p. baaa010, 2020. 24

    2020

  7. [15]

    S. A. Alowais et al., ”Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC medical education, vol. 23, no. 1, p. 689, 2023

    2023

  8. [16]

    B. C. Stahl, Artificial intelligence for a better future: an ecosys- tem perspective on the ethics of AI and emerging digital technologies . Springer Nature, 2021

    2021

  9. [17]

    R. G. Russo, ”Ethics and Professionalism,” in Medical Ethics : Springer, 2024, pp. 199-217

    2024

  10. [18]

    Zlateva, L

    P. Zlateva, L. Steshina, I. Petukhov, and D. Velev, ”A Conceptual Framework for Solving Ethical Issues in Generative Artificial Intel- ligence,” in Electronics, Communications and Networks : IOS Press, 2024, pp. 110-119

    2024

  11. [19]

    Tayebi Arasteh et al., ”Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging,” Communica- tions Medicine, vol

    S. Tayebi Arasteh et al., ”Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging,” Communica- tions Medicine, vol. 4, no. 1, p. 46, 2024

    2024

  12. [20]

    Shobarani, V

    R. Shobarani, V. S. Devi, D. Usha, M. Bharathi, S. Pratheepa, and K. S. Prakhash, ”Unraveling the Power of AI in Medical Imaging: A Deep Dive Into Image Processing,” in Enhancing Medical Imaging with Emerging Technologies: IGI Global, 2024, pp. 287-314

    2024

  13. [21]

    O. T. Gabriel, ”Data Privacy and Ethical Issues in Collecting Health Care Data Using Artificial Intelligence Among Health Workers,” Cen- ter for Bioethics and Research, 2023

    2023

  14. [22]

    C. Martin et al., ”The ethical considerations including inclusion and biases, data protection, and proper implementation among AI in ra- diology and potential implications,” Intelligence-Based Medicine, vol. 6, p. 100073, 2022

    2022

  15. [23]

    A. O. Adeniyi, J. O. Arowoogun, C. A. Okolo, R. Chidi, and O. Babawarun, ”Ethical considerations in healthcare IT: A review of data privacy and patient consent issues,” World Journal of Advanced Research and Reviews, vol. 21, no. 2, pp. 1660-1668, 2024

    2024

  16. [24]

    M. N. Duffourc and S. Gerke, ”Health Care AI and Patient Pri- vacy—Dinerstein v Google,” JAMA, 2024. 25

    2024

  17. [25]

    J. C. L. Ong et al., ”Ethical and regulatory challenges of large lan- guage models in medicine,” The Lancet Digital Health , 2024

    2024

  18. [26]

    Najjar, ”Redefining radiology: a review of artificial intelligence integration in medical imaging,” Diagnostics, vol

    R. Najjar, ”Redefining radiology: a review of artificial intelligence integration in medical imaging,” Diagnostics, vol. 13, no. 17, p. 2760, 2023

    2023

  19. [27]

    A. E. Obasa, ”The Ethics of Artificial Intelligence in Healthcare Set- tings,” 2023

    2023

  20. [28]

    Huang, Z

    C. Huang, Z. Zhang, B. Mao, and X. Yao, ”An overview of artificial intelligence ethics,” IEEE Transactions on Artificial Intelligence , vol. 4, no. 4, pp. 799-819, 2022

    2022

  21. [29]

    Kazim and A

    E. Kazim and A. S. Koshiyama, ”A high-level overview of AI ethics,” Patterns, vol. 2, no. 9, 2021

    2021

  22. [30]

    Sonko, A

    S. Sonko, A. O. Adewusi, O. C. Obi, S. Onwusinkwue, and A. Ata- doga, ”A critical review towards artificial general intelligence: Chal- lenges, ethical considerations, and the path forward,” World Journal of Advanced Research and Reviews, vol. 21, no. 3, pp. 1262-1268, 2024

    2024

  23. [31]

    Patel, ”Ethical reflections on data-centric AI: balancing benefits and risks,” International Journal of Artificial Intelligence Research and Development , vol

    K. Patel, ”Ethical reflections on data-centric AI: balancing benefits and risks,” International Journal of Artificial Intelligence Research and Development , vol. 2, no. 1, pp. 1-17, 2024

    2024

  24. [32]

    Franc´ es-G´ omez, ”Ethical Principles and Governance for AI,” in Ethics of Artificial Intelligence : Springer, 2024, pp

    P. Franc´ es-G´ omez, ”Ethical Principles and Governance for AI,” in Ethics of Artificial Intelligence : Springer, 2024, pp. 191-217

    2024

  25. [33]

    W. N. Price and I. G. Cohen, ”Privacy in the age of medical big data,” Nature medicine, vol. 25, no. 1, pp. 37-43, 2019

    2019

  26. [34]

    H. Chen, ”The ethical challenges of educational artificial intelligence and coping measures: A discussion in the context of the 2024 World Digital Education Conference,” Science Insights Education Frontiers, vol. 20, no. 2, pp. 3263-3281, 2024

    2024

  27. [35]

    Shafik, ”Toward a More Ethical Future of Artificial Intelligence and Data Science,” in The Ethical Frontier of AI and Data Analysis : IGI Global, 2024, pp

    W. Shafik, ”Toward a More Ethical Future of Artificial Intelligence and Data Science,” in The Ethical Frontier of AI and Data Analysis : IGI Global, 2024, pp. 362-388. 26

    2024

  28. [36]

    Saheb, ”Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis,” Science and Engineering Ethics , vol

    T. Saheb, ”Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis,” Science and Engineering Ethics , vol. 30, no. 2, p. 9, 2024

    2024

  29. [37]

    Hagendorff, ”The ethics of AI ethics: An evaluation of guidelines,” Minds and machines , vol

    T. Hagendorff, ”The ethics of AI ethics: An evaluation of guidelines,” Minds and machines , vol. 30, no. 1, pp. 99-120, 2020

    2020

  30. [38]

    G. A. H. Horta, Z. G. Garc ´ ıa, and A. de Paula Paredes, ”Ethics in the professional practice of imaging specialists,” in Seminars in Medical Writing and Education , 2023, vol. 2, pp. 39-39

    2023

  31. [39]

    L. W. Roberts, J. Battaglia, and R. S. Epstein, ”Frontier ethics: Men- tal health care needs and ethical dilemmas in rural communities,” Psychiatric services, vol. 50, no. 4, pp. 497-503, 1999

    1999

  32. [40]

    Filippi et al., ”Ethical considerations and fairness in the use of artificial intelligence for neuroradiology,” American Journal of Neu- roradiology, vol

    C. Filippi et al., ”Ethical considerations and fairness in the use of artificial intelligence for neuroradiology,” American Journal of Neu- roradiology, vol. 44, no. 11, pp. 1242-1248, 2023

    2023

  33. [41]

    J. B. Freymann, J. S. Kirby, J. H. Perry, D. A. Clunie, and C. C. Jaffe, ”Image data sharing for biomedical research—meeting HIPAA requirements for de-identification,” Journal of digital imaging, vol. 25, no. 1, pp. 14-24, 2012

    2012

  34. [42]

    Pesapane and P

    F. Pesapane and P. Summers, ”Ethics and regulations for AI in ra- diology,” in Artificial Intelligence for Medicine : Elsevier, 2024, pp. 179-192

    2024

  35. [43]

    Regulation, ”Regulation (EU) 2016/679 of the European Parlia- ment and of the Council,” Regulation (eu), vol

    P. Regulation, ”Regulation (EU) 2016/679 of the European Parlia- ment and of the Council,” Regulation (eu), vol. 679, p. 2016, 2016

    2016

  36. [44]

    Susithra, ”Ethical Considerations in Sharing Patient Data: A Sys- tematic Review,” Applying Machine Learning Techniques to Bioinfor- matics: Few-Shot and Zero-Shot Methods , pp

    K. Susithra, ”Ethical Considerations in Sharing Patient Data: A Sys- tematic Review,” Applying Machine Learning Techniques to Bioinfor- matics: Few-Shot and Zero-Shot Methods , pp. 201-216, 2024

    2024

  37. [45]

    S. C. Horii and W. D. Bidgood, ”DICOM: A standard for medical imaging,” in Enabling Technologies for High-Bandwidth Applications , 1993, vol. 1785: SPIE, pp. 87-102

    1993

  38. [46]

    N. Stogiannos et al., ”Black box no more: A cross-sectional multi- disciplinary survey for exploring governance and guiding adoption of 27 AI in medical imaging and radiotherapy in the UK,” International Journal of Medical Informatics , vol. 186, p. 105423, 2024

    2024

  39. [47]

    Grzybowski, K

    A. Grzybowski, K. Jin, and H. Wu, ”Challenges of artificial intelli- gence in medicine and dermatology,” Clinics in dermatology , 2024

    2024

  40. [48]

    artificial intelligence revolution

    F. Coppola et al., ”Human, all too human? An all-around appraisal of the “artificial intelligence revolution” in medical imaging,” Frontiers in psychology , vol. 12, p. 710982, 2021. World Journal of Advanced Research and Reviews., 21(3), 1262-1268

    2021

  41. [49]

    Zanca et al., ”Expanding the medical physicist curricular and pro- fessional programme to include Artificial Intelligence,” Physica Med- ica, vol

    F. Zanca et al., ”Expanding the medical physicist curricular and pro- fessional programme to include Artificial Intelligence,” Physica Med- ica, vol. 83, pp. 174-183, 2021

    2021

  42. [50]

    Lotan et al., ”Medical imaging and privacy in the era of artificial intelligence: myth, fallacy, and the future,” Journal of the American College of Radiology , vol

    E. Lotan et al., ”Medical imaging and privacy in the era of artificial intelligence: myth, fallacy, and the future,” Journal of the American College of Radiology , vol. 17, no. 9, pp. 1159-1162, 2020

    2020

  43. [51]

    Diaz-Asper, M

    C. Diaz-Asper, M. K. Hauglid, C. Chandler, A. S. Cohen, P. W. Foltz, and B. Elvev ˚ ag, ”A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions,” American Psychologist, vol. 79, no. 1, p. 79, 2024

    2024

  44. [52]

    Rosenstrauch, U

    D. Rosenstrauch, U. Mangla, A. Gupta, and C. T. Masau, ”Artificial Intelligence and Ethics,” inDigital Health Entrepreneurship: Springer, 2023, pp. 225-239

    2023

  45. [53]

    Franco D’Souza, M

    R. Franco D’Souza, M. Mathew, V. Mishra, and K. M. Surapaneni, ”Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education,”Medical Education Online, vol. 29, no. 1, p. 2330250, 2024

    2024

  46. [54]

    N. J. Economou-Zavlanos et al., ”Translating ethical and quality prin- ciples for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare,” Journal of the American Medical Informatics Association, vol. 31, no. 3, pp. 7...

    2024

  47. [55]

    F. Li, N. Ruijs, and Y. Lu, ”Ethics & AI: A systematic review on ethical concerns and related strategies for designing with AI in health- care,” Ai, vol. 4, no. 1, pp. 28-53, 2022

    2022

  48. [56]

    P. Esmaeilzadeh, ”Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations,” Artificial Intelligence in Medicine , vol. 151, p. 102861, 2024

    2024

  49. [57]

    Radanliev, O

    P. Radanliev, O. Santos, A. Brandon-Jones, and A. Joinson, ”Ethics and responsible AI deployment,” Frontiers in Artificial Intelligence , vol. 7, p. 1377011, 2024. 29

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.