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Cited by 4Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews

Open access
El Arab, Rabie Adel, Mustafa, Mohammad Hussein, Almagharbeh, Wesam Taher, Saleem, Noor Hafiz, Al Abdulmohsen, Shahad, Boathab, Ritaj, Bu Washl, Mohammed·2026·Healthcare
DOI 10.3390/healthcare14111459PMC PMC13256130
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Abstract

Background: Clinical artificial intelligence (AI) is rapidly moving from retrospective model development into prospective evaluation, implementation, and routine care. Existing reviews have addressed specific aspects of this transition, including monitoring, drift, implementation, governance, and human–AI interaction; however, these bodies of work remain methodologically and conceptually fragmented across different review traditions. Methods: We conducted a scoping review of review-level and review-oriented literature. We searched MEDLINE, Embase, Scopus, and Web of Science Core Collection from database inception to 28 February 2026. We charted review characteristics and conducted an inductive thematic synthesis of extracted review-level findings, while distinguishing operational, deployment-proximal, methodological, and conceptual/governance-oriented evidence. Results: We included 25 review-level publications spanning systematic, scoping, methodological, narrative, and governance-oriented reviews. Three major themes emerged. First, clinically important risks were consistently framed as socio-technical rather than purely algorithmic: trustworthiness depended not only on technical performance, but also on fairness, transparency, workflow fit, human oversight, and organisational readiness. Second, the included review literature consistently recommended post-deployment monitoring but showed limited operational maturity; monitoring methods, action thresholds, fairness surveillance, and corrective responses were weakly standardised, and mature evidence from activated systems in routine care remained sparse. Third, trustworthy implementation was increasingly framed as a lifecycle governance challenge extending beyond procurement and initial validation to include local validation, subgroup auditing, drift detection, controlled updating, incident response, and, where necessary, rollback or retirement. Discussion: The review literature suggests a persistent normative–operational gap, meaning that recommendations about what trustworthy clinical AI should require have advanced faster than evidence on how monitoring, updating, and governance are implemented in routine care. The strongest unresolved challenge is therefore not principal generation alone, but the translation of monitoring and governance expectations into actionable operational systems. Conclusions: Post-development trustworthiness in clinical AI should be understood as a lifecycle property, not a one-time technical achievement. Future work should prioritise stronger operational evidence, clearer reporting of deployment-proximal and post-deployment evaluation, methodological standardisation of monitoring metrics and thresholds, implementation research on feasible governance models, and evaluation frameworks for assessing post-deployment safety, fairness, accountability, and sustainability.

AI snapshot

Written from the paper’s full text.

TL;DR

This scoping review of 25 review-level publications maps post-development robustness, post-deployment monitoring, and lifecycle governance for clinical AI. It finds a persistent normative–operational gap: monitoring and governance are widely recommended but remain incompletely specified, unevenly operationalised, and supported by limited mature evidence from activated systems.

Key findings

  • Three themes recurred: clinically important risks were framed as socio-technical rather than purely algorithmic; post-deployment monitoring was widely advocated but operationally immature; and trustworthy implementation was increasingly framed as lifecycle governance extending beyond procurement and initial validation.
  • Monitoring evidence was sparse: Andersen et al. found only 9 of 39 sources described clinically tested or implemented monitoring methods, only 3 addressed fairness monitoring, and only 1 described clinical implementation of a fairness-monitoring method. Khan et al. found planning and evaluation addressed in 84% and 60% of 25 implementation frameworks, versus clinical integration in 52% and post-deployment improvement in 24%.
  • Reporting and equity gaps were documented: in 692 FDA Summary of Safety and Effectiveness Data documents, 3.6% reported race or ethnicity, 99.1% provided no socioeconomic data, 81.6% did not report age, and 37% reported sample size; 69 approvals (10.0%) were licensed for paediatric use, including 4 (0.6%) developed exclusively for children. Maimaitiaili et al. found the security and compliance layer had the lowest maturity score (mean 1.69) across a five-layer hospital AI architecture model.
  • Deployment-proximal and drift evidence showed limitations: Tikhomirov et al. reviewed 75 silent evaluations and found performance often declined from retrospective studies to prospective silent use; Guo et al. found calibration deterioration more commonly reported than discrimination deterioration, with no single updating method universally effective.

Methods

  • Scoping review of review-level and review-oriented literature; searched MEDLINE, Embase, Scopus, and Web of Science Core Collection from database inception to 28 February 2026; included 25 review-level publications.
  • Charted review characteristics and evidence proximity; conducted inductive thematic synthesis adapted from Thomas and Harden (2008); classified reviews into five analytic orientations (direct post-deployment monitoring, deployment-proximal evaluation, methodological drift/updating, governance/implementation/MLOps, and human-factors/fairness/robustness).
  • No formal critical appraisal was used as an inclusion threshold; interpretive weight was calibrated by review design, transparency, source type, and proximity to operational deployment. The protocol was not prospectively registered.

Limitations

  • Stated in the paper: the unit of analysis was the review publication, not primary studies or deployed systems, so the review cannot estimate prevalence, frequency, or magnitude of post-deployment failures; the corpus was methodologically heterogeneous; overlap of primary studies across reviews was considered qualitatively but not formally quantified; mature operational evidence from activated systems was limited; restriction to English-language, peer-reviewed journal publications may have missed regulatory, institutional, technical, and non-English sources; the field is evolving rapidly; and the proposed interpretive model is heuristic, not a validated framework or standard.
  • Evident from the text: no protocol registration and no formal critical appraisal further limit interpretive confidence; recurrence of themes may reflect shared underlying sources or repeated citation of influential conceptual papers rather than independent empirical convergence; and the review cannot determine which monitoring metrics, thresholds, update strategies, or governance models are most effective across settings.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/healthcare14111459

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

58 of 65 cited sources checked · 29 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    A systematic review showed marked between-hospital variability in performance, indicating that transportability cannot be assumed even for widely deployed systems.

    “A systematic review showed marked between-hospital variability in performance, indicating that transportability cannot be assumed even for widely deployed systems [9].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [9] Rockenschaub, P., Akay, E.M., Carlisle, B.G., Hilbert, A., Wendland, J., Meyer-Eschenbach, F., Näher, A.-F., Frey, D., and Madai, V.I. (2025). External validation of AI-based scoring systems in the ICU: A systematic review and meta-analysis. BMC Med. Inform. Decis. Mak., 25. source

      The Results section (‘Performance at new hospitals’; page unavailable) reports that external-validation performance varied substantially and was, on average, lower than internal-validation performance. The Discussion says this makes validation at new hospitals important. The review does not establish that the systems showing this variability were widely deployed; it describes widespread clinical adoption as a goal. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Premisecitation

    Clinical AI is increasingly moving beyond retrospective model development into prospective evaluation, implementation, and routine clinical care.

    “Clinical artificial intelligence (AI) is increasingly moving beyond retrospective model development into prospective evaluation, implementation, and routine clinical care [1,2].”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [1] Ogut, E. (2025). Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery. Clin. Pract., 15. source

      Results §3.2 says some large healthcare systems have started prospective trials of AI clinical decision support, supporting movement beyond retrospective evaluation. But Discussion §4.5 says most AI tools were evaluated retrospectively, and the Introduction and Conclusions describe integration into routine care as early-stage and ongoing. The claim overstates how far implementation and routine use have progressed. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [2] MacHeka, 2024, Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: A systematic review, BMJ Oncol. source

      The Conclusion (page unavailable) reports rapid growth in postdiagnostic cancer AI research and acknowledges that some tools have undergone prospective clinical evaluation. But it says only a small fraction have been evaluated prospectively and calls for future implementation research; WHAT THIS STUDY ADDS (page unavailable) says a lack of such research hinders clinical deployment. The review therefore does not establish an increasing move into implementation and routine care across clinical AI. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Premisecitation

    Strong development or vendor-reported performance does not necessarily translate into reliable clinical utility in practice.

    “These concerns are reinforced by empirical evidence showing that strong development or vendor-reported performance does not necessarily translate into reliable clinical utility in practice [6,7].”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [6] Chew, B.H., and Ngiam, K.Y. (2025). Artificial intelligence tool development: What clinicians need to know?. BMC Med., 23. source

      The Discussion (page unavailable) says that some AI models achieve expert-level accuracy but that weak study designs can overestimate performance claims; it calls for comparative evaluation to establish clinical utility. The AI/ML model validation section likewise distinguishes development performance from real-world clinical impact. These passages support the general caution, but they do not provide empirical evidence about vendor-reported performance specifically. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [7] Goldsack, 2020, Verification, analytical validation, and clinical validation (V3): The foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs), npj Digit. Med. source

      In “Moving from current siloed practices to one universal best practice” (page unavailable), the paper says a BioMeT may perform well under V3 yet be useless if the target population cannot use it appropriately in the intended setting. This supports the distinction between performance and clinical utility, but the cited passage does not provide empirical evidence about strong development or vendor-reported performance failing in practice. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    Reporting, evaluation, and appraisal frameworks including SPIRIT-AI, CONSORT-AI, DECIDE-AI, TRIPOD+AI, and PROBAST+AI have substantially strengthened the conceptual and reporting architecture for trustworthy clinical AI.

    “Reporting, evaluation, and appraisal frameworks including SPIRIT-AI, CONSORT-AI, DECIDE-AI, TRIPOD+AI, and PROBAST+AI have substantially strengthened the conceptual and reporting architecture for trustworthy clinical AI [10,11,12,13,14].”
    Quote in the paperNot a substantial claim5 of 5 sources checked
    • cites [10] Rivera, 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI Extension, BMJ source

      The Abstract identifies SPIRIT-AI as a reporting guideline for AI trial protocols, and the Discussion says it is expected, alongside CONSORT-AI, to improve trial quality. The source does not establish that these frameworks have already substantially strengthened clinical AI reporting, and it does not address the other frameworks named in the sentence. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [11] Liu, 2020, Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension, Nat. Med. source
    • cites [12] Vasey, 2022, Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI, BMJ source

      The Abstract says DECIDE-AI was developed to improve reporting of early clinical evaluations of AI decision-support systems, supporting its place among clinical AI reporting guidelines. But the paper says the guideline’s impact on reporting standards still needs to be assessed; it does not establish that DECIDE-AI has already substantially strengthened them. This verdict concerns DECIDE-AI, the framework this source documents, rather than the other separately cited frameworks. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [13] Moons, 2025, PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods, BMJ source
    • cites [14] Collins, 2024, TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods, BMJ source

      The reporting-guidelines table identifies SPIRIT-AI, CONSORT-AI, DECIDE-AI, and TRIPOD+AI and their distinct roles; the Discussion says TRIPOD+AI can improve transparency and appraisal. But the Development of TRIPOD+AI section describes PROBAST+AI as a tool under development, and the paper does not establish that the frameworks collectively have substantially strengthened trustworthy clinical AI. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Study design and conductcitation

    Sources were not treated as methodologically equivalent; their review design, transparency of methods, evidential orientation, and proximity to operational deployment were explicitly charted and used to calibrate interpretive weight during synthesis and discussion.

    “These sources were not treated as methodologically equivalent; instead, their review design, transparency of methods, evidential orientation, and proximity to operational deployment were explicitly charted and used to calibrate interpretive weight during synthesis and discussion [26].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [26] Pham, 2014, A scoping review of scoping reviews: Advancing the approach and enhancing the consistency, Res. Synth. Methods source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
  • May combine two claims. 27
  • Classified as probably not a substantial claim. 37
  • The quote only partly states this claim. 2

  • May combine two claims.

    Premisecitation

    Independent external validation of the Epic Sepsis Model found poor discrimination and calibration, low sensitivity relative to contemporary care, and substantial potential alert burden despite widespread implementation.

    “Independent external validation of the Epic Sepsis Model found poor discrimination and calibration, low sensitivity relative to contemporary care, and substantial potential alert burden despite widespread implementation [8].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [8] Wong, 2021, External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients, JAMA Intern Med. source

  • May combine two claims.

    Premisecitation

    Much of this literature addresses governance architectures, robustness concepts, implementation barriers, or monitoring frameworks, whereas only a subset is centred on longitudinal or operational evidence generated during routine deployment use.

    “Much of this literature addresses governance architectures, robustness concepts, implementation barriers, or monitoring frameworks, whereas only a subset is centred on longitudinal or operational evidence generated during routine deployment use [18,19,20].”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites [18] Andersen, 2024, Monitoring performance of clinical artificial intelligence in health care: A scoping review, JBI Evid. Synth. source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [19] Rajagopal, 2024, Machine Learning Operations in Health Care: A Scoping Review, Mayo Clin. Proc. Digit. Health source
    • cites [20] Tikhomirov, 2026, A scoping review of silent trials for medical artificial intelligence, Nat. Health source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The review was designed consistent with Joanna Briggs Institute guidance and PRISMA-ScR reporting standards.

    “Consistent with Joanna Briggs Institute guidance and PRISMA-ScR reporting standards, the review was designed to characterise the scope and structure of the available literature, identify recurrent themes, and distinguish between conceptually oriented, deployment-proximal, and operationally grounded review traditions [21,22,23,24].”
    Quote in the paperNot a substantial claim2 of 4 sources checked
    • cites [21] Munn, Z., Peters, M.D.J., Stern, C., Tufanaru, C., McArthur, A., and Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med. Res. Methodol., 18. source

      The ‘Indications for scoping reviews’ section supports designing a review to map the scope, focus, and types of available literature. The Discussion mentions Joanna Briggs Institute guidance and the recent PRISMA extension for scoping reviews, but does not set out PRISMA-ScR reporting standards or the claimed three-way classification of review traditions. Our copy has no page numbers for these passages. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [22] Peters, 2020, Updated methodological guidance for the conduct of scoping reviews, JBI Evid. Synth. source
    • cites [23] Tricco, 2018, PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation, Ann. Intern Med. source
    • cites [24] Aromataris, E., Lockwood, C., Porritt, K., Pilla, B., and Jordan, Z. (2024). JBI Manual for Evidence Synthesis, JBI. source

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    The review used a cautious inductive thematic synthesis approach adapted from Thomas and Harden (2008).

    “we used a cautious inductive thematic synthesis approach adapted from Thomas and Harden (2008) [25]”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [25] Thomas, J., and Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med. Res. Methodol., 8. source

  • May combine two claims.

    Premisecitation

    Global guidance emphasises equity, capacity, and context-sensitive governance for AI in health.

    “this concern is reinforced by evidence that clinical AI implementation-framework authorship has been concentrated in high-income settings and by global guidance emphasising equity, capacity, and context-sensitive governance for AI in health [49,52].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [49] Khan, S.D., Hoodbhoy, Z., Raja, M.H.R., Kim, J.Y., Hogg, H.D.J., Manji, A.A.A., Gulamali, F., Hasan, A., Shaikh, A., and Tajuddin, S. (2024). Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLoS Digit. Health, 3. source
    • cites [52] World Health Organization (2025, May 20). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. 2021, 1–148. Available online: https://www.who.int/publications/i/item/9789240029200.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Premisecitation

    Recent implementation-oriented perspectives emphasise that safe deployment depends on structured governance, staged review, and durable post-deployment accountability rather than one-time technical approval alone.

    “Recent implementation-oriented perspectives make a similar point by emphasising that safe deployment depends on structured governance, staged review, and durable post-deployment accountability rather than one-time technical approval alone [15,50,51].”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites [15] You, 2025, Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications, npj Digit. Med. source
    • cites [50] Wells, 2025, A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare, npj Digit. Med. source
    • cites [51] Bedoya, 2022, A framework for the oversight and local deployment of safe and high-quality prediction models, J. Am. Med. Inform. Assoc. source

  • May combine two claims.

    Interpretationcitation

    These assumptions may not hold equally across low- and middle-income countries, smaller hospitals, rural systems, or institutions with limited digital maturity, where the infrastructure required for continuous monitoring, subgroup surveillance, drift detection, formal change control, and incident response may be more difficult to sustain.

    “These assumptions may not hold equally across low- and middle-income countries, smaller hospitals, rural systems, or institutions with limited digital maturity, where the infrastructure required for continuous monitoring, subgroup surveillance, drift detection, formal change control, and incident response may be more difficult to sustain [49,50,51,52].”
    Quote in the paperSubstantial claim3 of 4 sources checked
    • cites [49] Khan, S.D., Hoodbhoy, Z., Raja, M.H.R., Kim, J.Y., Hogg, H.D.J., Manji, A.A.A., Gulamali, F., Hasan, A., Shaikh, A., and Tajuddin, S. (2024). Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLoS Digit. Health, 3. source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [50] Wells, 2025, A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare, npj Digit. Med. source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [51] Bedoya, 2022, A framework for the oversight and local deployment of safe and high-quality prediction models, J. Am. Med. Inform. Assoc. source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [52] World Health Organization (2025, May 20). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. 2021, 1–148. Available online: https://www.who.int/publications/i/item/9789240029200.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • The authors state this as an assumption: Lifecycle governance requires access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures.

  • Classified as probably not a substantial claim.

    Findingown data

    Three major themes emerged from the synthesis.

    “Three major themes emerged.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The included review literature consistently recommended post-deployment monitoring but showed limited operational maturity.

    “Second, the included review literature consistently recommended post-deployment monitoring but showed limited operational maturity”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    Trustworthy implementation was increasingly framed as a lifecycle governance challenge extending beyond procurement and initial validation to include local validation, subgroup auditing, drift detection, controlled updating, incident response, and, where necessary, rollback or retirement.

    “Third, trustworthy implementation was increasingly framed as a lifecycle governance challenge extending beyond procurement and initial validation to include local validation, subgroup auditing, drift detection, controlled updating, incident response, and, where necessary, rollback or retirement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Clinically important risks were consistently framed as socio-technical rather than purely algorithmic; trustworthiness depended not only on technical performance, but also on fairness, transparency, workflow fit, human oversight, and organisational readiness.

    “First, clinically important risks were consistently framed as socio-technical rather than purely algorithmic: trustworthiness depended not only on technical performance, but also on fairness, transparency, workflow fit, human oversight, and organisational readiness.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The corpus was methodologically heterogeneous and comprised systematic reviews, scoping reviews, one systematic review with meta-analysis, and broader review-oriented papers addressing governance, implementation, methodological, and human-factors aspects of clinical AI.

    “The corpus was methodologically heterogeneous and comprised systematic reviews, scoping reviews, one systematic review with meta-analysis, and broader review-oriented papers addressing governance, implementation, methodological, and human-factors aspects of clinical AI.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    Deployment-proximal reviews, including silent-trial and shadow-mode evaluations, informed conclusions about pre-activation readiness.

    “Deployment-proximal reviews, including silent-trial and shadow-mode evaluations, informed conclusions about pre-activation readiness.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    Three broad governance domains repeatedly appear across translational stages: technical validity, human and workflow integration, and institutional governance capacity.

    “A second interpretive feature of the synthesis was the repeated appearance of three broad governance domains across translational stages.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    Governance, implementation, readiness, and MLOps reviews informed lifecycle oversight considerations.

    “Governance, implementation, readiness, and MLOps reviews informed lifecycle oversight considerations.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingnone given

    Technical validity includes conventional performance measures such as discrimination, calibration, and generalisability, and also temporal stability, subgroup performance, updating logic, and vulnerability to dataset shift or performance decay.

    “The first is technical validity, which includes not only conventional performance measures such as discrimination, calibration, and generalisability, but also temporal stability, subgroup performance, updating logic, and vulnerability to dataset shift or performance decay.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Post-deployment monitoring was widely advocated across the included reviews, but examples of mature operational implementation were limited within the review literature.

    “Second, post-deployment monitoring was widely advocated, but examples of mature operational implementation were limited within the review literature.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingreasoning

    Methodological reviews of drift, recalibration, retraining, and updating informed conclusions about temporal instability and adaptive risk.

    “Methodological reviews of drift, recalibration, retraining, and updating informed conclusions about temporal instability and adaptive risk.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingreasoning

    Human-factors, fairness, trustworthiness, and broader robustness-oriented reviews informed socio-technical dimensions of post-development trustworthiness.

    “Human-factors, fairness, trustworthiness, and broader robustness-oriented reviews informed socio-technical dimensions of post-development trustworthiness.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingreasoning

    Human and workflow integration includes the way outputs are presented, interpreted, verified, overridden, and incorporated into clinical decision pathways, together with risks introduced by alert burden, time pressure, automation bias, poor usability, and weak trust calibration.

    “The second is human and workflow integration, which includes the way outputs are presented, interpreted, verified, overridden, and incorporated into clinical decision pathways, together with the risks introduced by alert burden, time pressure, automation bias, poor usability, and weak trust calibration.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    A persistent normative-operational gap exists in the corpus: local validation, post-deployment monitoring, human oversight, subgroup surveillance, and change management were widely recommended, yet operational examples remained comparatively sparse and weakly standardised.

    “A further recurring pattern in the corpus was a persistent normative–operational gap. According to the reviews included, local validation, post-deployment monitoring, human oversight, subgroup surveillance, and change management were widely recommended, yet operational examples remained comparatively sparse and weakly standardised.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    The included corpus was methodologically heterogeneous and varied in evidential strength, with some reviews grounded predominantly in empirical studies and others oriented more towards conceptual, governance, or framework-based literature.

    “Second, the included corpus was methodologically heterogeneous and varied in evidential strength, with some reviews grounded predominantly in empirical studies and others oriented more towards conceptual, governance, or framework-based literature.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingreasoning

    Institutional governance capacity includes the organisational ability to monitor, interpret, escalate, document, investigate, and respond to emerging risks through formal review cadence, change control, accountable ownership, and, where necessary, rollback, restriction, or retirement.

    “The third is institutional governance capacity, which includes the organisational ability to monitor, interpret, escalate, document, investigate, and respond to emerging risks through formal review cadence, change control, accountable ownership, and, where necessary, rollback, restriction, or retirement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    The published review literature remains methodologically heterogeneous and contains limited mature operational evidence from activated systems in routine clinical care.

    “At the same time, the published review literature remains methodologically heterogeneous and contains limited mature operational evidence from activated systems in routine clinical care.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingreasoning

    The included reviews suggest a recurring set of governance considerations for high-stakes clinical AI: local validation before activation; prospective specification of monitoring targets, review cadence, and accountability; predefined escalation and change-control procedures; mechanisms for incident reporting and investigation; attention to human factors and workflow effects; subgroup-aware性能?

    “The reviews included suggest a recurring set of governance considerations for high-stakes clinical AI.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Post-development trustworthiness in clinical AI should be understood as a lifecycle property, not a one-time technical achievement.

    “Post-development trustworthiness in clinical AI should be understood as a lifecycle property, not a one-time technical achievement.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Methodological features relevant to interpretive confidence were explicitly considered during charting and discussion.

    “However, because the review also incorporated interpretive synthesis, methodological features relevant to interpretive confidence were explicitly considered during charting and discussion.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Greater interpretive weight was given to reviews with clearer methods and stronger empirical grounding.

    “Greater interpretive weight was given to reviews with clearer methods and stronger empirical grounding.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Interpretive claims were deliberately constrained by the heterogeneity of the included review corpus and by the fact that the unit of analysis was the review publication rather than the underlying primary study.

    “Throughout the synthesis, interpretive claims were deliberately constrained by the heterogeneity of the included review corpus and by the fact that the unit of analysis was the review publication rather than the underlying primary study.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Narrative and review-oriented publications were retained for conceptual and governance mapping but were not interpreted as carrying the same evidential weight as more methodologically explicit systematic, scoping, or meta-analytic reviews.

    “Accordingly, narrative and review-oriented publications were retained for conceptual and governance mapping but were not interpreted as carrying the same evidential weight as more methodologically explicit systematic, scoping, or meta-analytic reviews.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Eligibility criteria were developed a priori and structured around the review question rather than a conventional intervention-comparator-outcome framework.

    “Eligibility criteria (Table 1) were developed a priori and were structured around the review question rather than a conventional intervention–comparator–outcome framework.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The findings of this review should be interpreted as recurrent emphases within a heterogeneous review corpus rather than as direct estimates of the prevalence, effectiveness, or operational maturity of post-deployment practices in the field.

    “These findings should be interpreted as recurrent emphases within a heterogeneous review corpus rather than as direct estimates of the prevalence, effectiveness, or operational maturity of post-deployment practices in the field.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    Methods were specified a priori and applied consistently throughout the review process.

    “These methods were specified a priori and were applied consistently throughout the review process.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Systematic reviews, scoping reviews, umbrella reviews, meta-analyses, and methodologically explicit reviews were given greater weight when drawing conclusions about evidence maturity, operational implementation, and empirical support.

    “Systematic reviews, scoping reviews, umbrella reviews, meta-analyses, and methodologically explicit reviews were given greater weight when drawing conclusions about evidence maturity, operational implementation, and empirical support.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Direct operational review evidence was distinguished from deployment-proximal, methodological, governance-oriented, and conceptual review evidence, and interpretive claims were calibrated accordingly.

    “At all stages, direct operational review evidence was distinguished from deployment-proximal, methodological, governance-oriented, and conceptual review evidence, and interpretive claims were calibrated accordingly.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Themes were not generated from narrative or opinion-based reviews alone; higher-order themes were retained only when supported across more than one review tradition or when clearly identified as conceptual rather than operational.

    “Themes were not generated from narrative or opinion-based reviews alone; higher-order themes were retained only when supported across more than one review tradition or when clearly identified as conceptual rather than operational.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The protocol was not prospectively registered.

    “The protocol was not prospectively registered, which should be considered when interpreting the conduct of the review.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    A protocol specifying the review question, eligibility criteria, search strategy, charting framework, and synthesis approach was developed before study selection.

    “A protocol specifying the review question, eligibility criteria, search strategy, charting framework, and synthesis approach was developed before study selection.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Interpretive claims were calibrated to the design and evidential character of the included reviews, and conclusions were framed as review-level observations rather than definitive estimates of real-world effect.

    “Because the included corpus was methodologically heterogeneous, interpretive claims were calibrated to the design and evidential character of the included reviews, and conclusions were framed as review-level observations rather than definitive estimates of real-world effect.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationnone given

    Automation bias, alert burden, poor usability, misplaced trust, and unclear uncertainty communication may compromise the safety of human-AI collaboration.

    “because automation bias, alert burden, poor usability, misplaced trust, and unclear uncertainty communication may compromise the safety of human–AI collaboration.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The principal implication is not that post-deployment governance is absent, but that, within the available review literature, it remains incompletely specified, unevenly operationalised, and insufficiently supported by mature operational evidence.

    “The principal implication is therefore not that post-deployment governance is absent, but that, within the available review literature, it remains incompletely specified, unevenly operationalised, and insufficiently supported by mature operational evidence.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    By synthesising review-level literature spanning post-deployment monitoring, governance, implementation, and human factors, the review brings into a single analytic frame strands of scholarship that are often examined separately.

    “By synthesising review-level literature spanning post-deployment monitoring, governance, implementation, and human factors, it brings into a single analytic frame strands of scholarship that are often examined separately.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    The field is evolving rapidly, particularly in relation to adaptive systems, generative AI, and post-market governance; accordingly, this synthesis should be read as temporally situated.

    “Finally, the field is evolving rapidly, particularly in relation to adaptive systems, generative AI, and post-market governance; accordingly, this synthesis should be read as temporally situated.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The review-of-reviews design allowed comparisons of how different review traditions conceptualise the translational challenge in clinical AI beyond model development, thereby clarifying areas of convergence, tension, and evidential immaturity across a heterogeneous field.

    “The review-of-reviews design also allowed comparisons of how different review traditions conceptualise the translational challenge in clinical AI beyond model development, thereby clarifying areas of convergence, tension, and evidential immaturity across a heterogeneous field.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The conclusions should be interpreted as a synthesis of review-level patterns, emphases, and maturity gaps, rather than as pooled estimates of the prevalence, magnitude, or certainty of post-deployment harms.

    “The conclusions should therefore be interpreted as a synthesis of review-level patterns, emphases, and maturity gaps, rather than as pooled estimates of the prevalence, magnitude, or certainty of post-deployment harms.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Restriction to English-language, peer-reviewed journal publications may have reduced capture of relevant regulatory guidance, institutional governance documents, technical standards, and non-English implementation studies that are particularly pertinent to post-deployment oversight.

    “Fifth, restriction to English-language, peer-reviewed journal publications may have reduced capture of relevant regulatory guidance, institutional governance documents, technical standards, and non-English implementation studies that are particularly pertinent to post-deployment oversight.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Because the included reviews spanned markedly different AI modalities, clinical tasks, evidential purposes, and implementation contexts, thematic recurrence should not be interpreted as implying uniform relevance across all domains of clinical AI.

    “Sixth, because the included reviews spanned markedly different AI modalities, clinical tasks, evidential purposes, and implementation contexts, thematic recurrence should not be interpreted as implying uniform relevance across all domains of clinical AI.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The governance considerations are synthesis-derived propositions rather than a validated checklist, consensus standard, or formal regulatory instrument.

    “These considerations should be interpreted as synthesis-derived propositions rather than as a validated checklist, consensus standard, or formal regulatory instrument.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Model performance, calibration, workflow fit, and subgroup validity may vary across institutions and populations.

    “particularly because model performance, calibration, workflow fit, and subgroup validity may vary across institutions and populations.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The problem is not only that institutions may lack technical tools, but also that they may lack the organisational conditions required to transform monitoring signals into accountable action: access to local data, agreed metrics, labelling strategies, defined ownership, review cadence, escalation thresholds, and authority to intervene.

    “The problem is not only that institutions may lack technical tools, but also that they may lack the organisational conditions required to transform monitoring signals into accountable action: access to local data, agreed metrics, labelling strategies, defined ownership, review cadence, escalation thresholds, and authority to intervene.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Study design and conductreasoning

    The broader vantage point enabled identification of recurrent cross-domain concerns that may be less apparent in narrower single-topic syntheses, particularly the repeated emphasis on lifecycle governance, socio-technical dimensions of trustworthiness, and the limited operational maturity of post-deployment monitoring.

    “This broader vantage point enabled identification of recurrent cross-domain concerns that may be less apparent in narrower single-topic syntheses, particularly the repeated emphasis on lifecycle governance, socio-technical dimensions of trustworthiness, and the limited operational maturity of post-deployment monitoring.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    The proposed interpretive model and operational considerations are heuristic outputs of a review-level synthesis; they are not validated instruments, consensus standards, or regulatory requirements.

    “Fourth, the proposed interpretive model and operational considerations are heuristic outputs of a review-level synthesis; they are not validated instruments, consensus standards, or regulatory requirements.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The three governance domains are analytically separable but operationally interdependent.

    “These domains are analytically separable but operationally interdependent: technical performance may appear stable while workflow harms accumulate; fairness may be endorsed rhetorically while subgroup monitoring remains infeasible; and monitoring may detect deterioration without any institutional mechanism capable of acting on it.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Because mature operational evidence from activated systems remains limited, conclusions about post-deployment trustworthiness should be read as evidence-informed and hypothesis-generating rather than definitive.

    “Finally, because mature operational evidence from activated systems remains limited, conclusions about post-deployment trustworthiness should be read as evidence-informed and hypothesis-generating rather than definitive.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    As a scoping synthesis of review-level and review-oriented literature, the review synthesises published review interpretations rather than primary-study outcomes directly.

    “First, as a scoping synthesis of review-level and review-oriented literature, it synthesises published review interpretations rather than primary-study outcomes directly.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductreasoning

    Overlap of primary studies across included reviews was considered qualitatively but not formally quantified; consequently, the apparent recurrence of some themes may partly reflect shared underlying source material rather than wholly independent convergence.

    “Third, overlap of primary studies across included reviews was considered qualitatively but not formally quantified; consequently, the apparent recurrence of some themes may partly reflect shared underlying source material rather than wholly independent convergence.”
    Quote in the paperSubstantial claim
56 of 56

  • The authors state this as an assumption.

    Lifecycle governance requires access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures.

    “The feasibility of lifecycle governance is likely to vary substantially across health systems because many recommendations assume access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures [49,50,51,52].”
1 of 1

Cites

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Authors
El Arab, R. A., Mustafa, M. H., Almagharbeh, W. T., Saleem, N. H., Al Abdulmohsen, S., Boathab, R., & Bu Washl, M.
Affiliations
  • Almoosa College of Health Sciences, Al Ahsa 36422, Saudi Arabia
  • Dr. Sulaiman Alhabib Medical Group, Riyadh 71491, Saudi Arabia
  • University of Tabuk (SA)
  • Nursing Department, Fatima College of Health Sciences, Abu Dhabi P.O. Box 3798, United Arab Emirates
ORCID
Venue
Healthcare
Publisher
MDPI AG
Published
2026-05-25
Volume
14
Issue
11
Pages
1459
ISSN
2227-9032 (ISSN-L)
DOI
10.3390/healthcare14111459
PMC
PMC13256130
OpenAlex
W7162289191
Semantic Scholar
5a0db5e508de9a28f21139272c6f62ddee0a391d
Cited by
4
References
47
Citations by year
2026: 4
FWCI
7.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Healthcare (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
Almoosa College of Health Sciences, Saudi Arabia
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
clinical AI, post-deployment monitoring, AI lifecycle governance, model drift detection, trustworthy AI, human oversight, incident response

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-13.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-13.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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