The Clinical AI Readiness Score (CARS): A Proposed Framework for Assessing AI Deployment Readiness in Healthcare

The Clinical AI Readiness Score (CARS): A Proposed Framework for Assessing Artificial Intelligence Deployment Readiness in Healthcare Settings

Published by: Clinical AI Academy
Contact: contact@clinicalaiacademy.com
Publication Date: June 27, 2025

Abstract

Background: Despite significant advances in artificial intelligence (AI) research for healthcare applications, the translation from research to clinical practice remains limited. Current frameworks primarily focus on development guidelines or reporting standards rather than providing comprehensive readiness assessment tools for deployment decisions.

Objective: To develop and propose the Clinical AI Readiness Score (CARS), a comprehensive conceptual framework for assessing the deployment readiness of AI systems in healthcare settings, addressing technical, clinical, ethical, and operational dimensions.

Methods: We conducted a systematic literature review and analysis of existing AI governance frameworks including FUTURE-AI, CLAIM, WHO/ITU guidelines, SUDO, FURM, TEHAI, and Health Care AI Toolkit. Through synthesis of best practices and gap analysis, we developed a conceptual 15-dimension assessment framework.

Results: The proposed CARS framework provides a unified conceptual approach that integrates critical dimensions often addressed separately in existing frameworks. The framework addresses significant gaps including integrated assessment tools, post-deployment monitoring, comprehensive stakeholder engagement, and systematic risk management.

Conclusions: The CARS framework represents a novel conceptual contribution to healthcare AI governance, proposing an integrated approach to deployment readiness assessment. Future research should focus on validating the framework through expert consensus studies, pilot implementations, and longitudinal outcome assessments.

Keywords: artificial intelligence, healthcare, clinical decision support, AI governance, deployment readiness, risk assessment

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Introduction

The integration of artificial intelligence into healthcare has accelerated dramatically, yet a critical gap exists between research achievements and clinical implementation.

The integration of artificial intelligence into healthcare has accelerated dramatically, with AI systems demonstrating remarkable capabilities across medical imaging, clinical decision support, drug discovery, and patient monitoring. However, a critical gap exists between research achievements and clinical implementation, with many promising AI systems failing to achieve successful real-world deployment.

Current AI governance frameworks primarily address either development guidance or research reporting standards, leaving healthcare organizations without practical tools for assessing deployment readiness. The FUTURE-AI framework provides comprehensive principles for trustworthy AI but lacks specific assessment criteria for deployment decisions. Similarly, reporting guidelines like CLAIM ensure transparent research communication but do not address operational readiness requirements.

The Clinical AI Readiness Score (CARS) framework addresses these challenges by proposing a comprehensive, integrated assessment tool specifically designed for deployment readiness evaluation. Unlike existing frameworks that focus on development guidance or research reporting, CARS targets the critical decision point where healthcare organizations must determine whether an AI system is ready for clinical implementation.

Methods

Framework Development Approach

The development of CARS followed a systematic, literature-based approach consisting of three phases: (1) systematic literature review and framework analysis, (2) gap analysis and dimension synthesis, and (3) conceptual framework construction and refinement.

Literature Review and Analysis

We conducted a comprehensive systematic review of existing AI governance frameworks, identifying 47 relevant frameworks published between 2018 and 2025. Particular attention was paid to frameworks achieving significant adoption in the healthcare AI community, including FUTURE-AI, CLAIM, WHO/ITU guidelines, SUDO, FURM, TEHAI, and various healthcare AI toolkits.

Gap Analysis and Synthesis

Systematic analysis revealed critical gaps in current approaches to AI readiness assessment. Most frameworks focused on either development guidance or reporting standards, with limited attention to deployment readiness requirements. Through detailed gap analysis, we identified fifteen critical dimensions organized into four categories: Technical Foundations, Clinical Validation, Ethical and Social Considerations, and Operational Readiness.

Study Limitations: This framework development was based entirely on literature synthesis without empirical validation through expert consensus, pilot testing, or outcome assessment. Future empirical validation will be necessary to establish practical utility and predictive validity.

Results: The Proposed CARS Framework

Framework Overview

The CARS framework consists of fifteen comprehensive assessment dimensions addressing technical, clinical, ethical, and operational requirements for successful AI deployment.

The CARS framework consists of fifteen comprehensive assessment dimensions addressing technical, clinical, ethical, and operational requirements for successful AI deployment. Each dimension is formulated as a specific assessment question designed to evaluate critical aspects of AI readiness based on documented evidence.

Technical Foundations

1. Data Quality and Governance: Is the data used to train and validate the AI model clearly documented, ethically sourced, representative of the target patient population, and of sufficient quality and quantity for the intended clinical application?
2. Explainability and Interpretability: Can the AI model's decision-making process be sufficiently understood by clinicians? Are there mechanisms to explain why the AI reached a specific conclusion for an individual patient?
3. Robustness and Performance: Has the AI model been rigorously tested for performance across diverse datasets, different clinical settings, and various patient subgroups, including edge cases and potential adversarial attacks?
4. Uncertainty Quantification: Does the AI model provide a reliable indication of its confidence or uncertainty for each prediction, communicated in an understandable and actionable manner?

Clinical Validation

5. Clinical Evidence and Validation: Is there robust evidence from well-designed clinical studies demonstrating the AI model's safety, accuracy, and actual clinical benefit in its intended use case?
6. Intended Use Definition: Is the specific intended clinical use of the AI model unequivocally defined, including the target patient population, clinical conditions, known limitations, and contraindications?

Ethical and Social Considerations

7. Fairness and Bias Assessment: Has the AI model been systematically audited for potential biases related to demographic factors, socioeconomic status, or other relevant characteristics, with effective mitigation strategies in place?
8. Regulatory and Ethical Compliance: Does the AI model comply with all relevant regulatory requirements and established ethical guidelines for AI in healthcare?
9. Data Security and Privacy: Are robust technical and organizational measures in place to ensure the security, integrity, and privacy of patient data used by the AI model?
10. Stakeholder Engagement: Have all relevant stakeholders been involved in the selection, development, or validation process, with adequate ongoing training provided for all users?

Operational Readiness

11. Usability and Workflow Integration: Is the AI tool designed for seamless integration into existing clinical workflows, with usability tested by representative clinical users?
12. Traceability and Auditability: Can the AI model's predictions, input data, and version be logged and audited, with clear records of model development and ongoing performance monitoring?
13. Post-deployment Monitoring and Governance: Is there a comprehensive plan for continuous monitoring, evaluation, and governance of the AI model's performance after deployment?
14. Risk-Benefit Assessment: Has a thorough assessment been conducted to weigh the potential clinical benefits against potential risks, including misdiagnosis, over-reliance, or introduction of new types of errors?
15. Contingency Planning: Are there established protocols and contingency plans if the AI system fails, provides erroneous information, or becomes unavailable due to technical issues?

Proposed Scoring Methodology

The CARS framework can be implemented using binary (0/1) or scaled (0-4) scoring approaches. For binary scoring, an overall readiness score is calculated as the percentage of dimensions meeting readiness criteria, with a proposed threshold of 80% for deployment readiness. For scaled scoring, threshold scores of 2.5 for basic readiness, 3.0 for good readiness, and 3.5 for excellent readiness are proposed.

Important Note: These scoring methodologies require empirical validation through future research to establish reliability, validity, and practical utility.

Discussion

Principal Contributions

The CARS framework represents a significant conceptual advancement in healthcare AI governance by providing the first comprehensive, integrated framework specifically designed for deployment readiness assessment. Unlike existing frameworks that address individual aspects of AI governance, CARS provides holistic assessment across all critical dimensions necessary for successful clinical implementation.

Comparison with Existing Frameworks

CARS builds upon existing frameworks while addressing their limitations. The FUTURE-AI framework provides valuable principles but lacks specific assessment criteria. The FURM framework addresses only three dimensions compared to CARS' comprehensive fifteen-dimension approach. Reporting guidelines like CLAIM serve important but distinct purposes from deployment readiness assessment.

Framework Coverage Analysis

Our analysis reveals that existing frameworks provide incomplete coverage of deployment readiness requirements. FUTURE-AI addresses 6/15 CARS dimensions, CLAIM addresses 8/15, WHO/ITU guidelines address 12/15, while no single framework provides comprehensive coverage across all critical dimensions.

Implications for Healthcare AI

The CARS framework has important implications for healthcare organizations, AI developers, and regulatory agencies. For healthcare organizations, it provides systematic deployment evaluation criteria. For AI developers, it establishes clear readiness expectations. For regulatory agencies, it offers structured assessment approaches that could inform regulatory processes.

Limitations and Future Directions

Several limitations should be acknowledged. First, the framework requires comprehensive empirical validation before practical implementation. Second, domain-specific adaptations may be necessary for certain clinical applications. Third, the framework assumes organizational maturity and resources that may not be available in all settings.

Critical Next Steps for Future Research:

  1. Expert Consensus Validation: Structured Delphi studies involving healthcare AI researchers, clinical practitioners, and implementation specialists
  2. Pilot Implementation Studies: Testing framework utility across diverse healthcare settings
  3. Criterion Validity Assessment: Longitudinal studies tracking relationships between CARS assessments and implementation outcomes
  4. Domain-Specific Adaptations: Development of specialized versions for specific clinical domains
  5. Scoring Methodology Validation: Empirical studies to establish appropriate scoring thresholds

Conclusion

The CARS framework addresses a critical gap in healthcare AI governance by proposing a comprehensive, conceptual framework for deployment readiness assessment. Through systematic analysis of existing frameworks, we have developed a fifteen-dimension assessment framework that integrates technical, clinical, ethical, and operational considerations essential for successful AI implementation.

The framework's unique conceptual contributions include comprehensive integration of multiple governance dimensions, specific focus on deployment readiness, and emphasis on post-deployment considerations. By providing clear assessment criteria, CARS offers a theoretical foundation for informed deployment decisions based on comprehensive evaluation.

The ultimate success of CARS will depend on rigorous empirical validation and subsequent adoption by healthcare organizations, AI developers, and other stakeholders. Only through systematic validation can this conceptual framework evolve into a practical tool that accelerates responsible adoption of beneficial AI technologies while ensuring appropriate attention to safety, ethics, and quality considerations essential for successful healthcare AI implementation.

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References

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[2] Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 1-9.
[3] Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.
[4] Lekadir, K., Frangi, A. F., Porras, A. R., et al. (2025). FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 388, e081554.
[5] Mongan, J., Moy, L., & Kahn Jr, C. E. (2020). Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiology: Artificial Intelligence, 2(2), e200029.
[6] Shah, N. H., Entwistle, D., & Pfeffer, M. A. (2024). Standing on FURM ground: a framework for evaluating fair, useful, and reliable AI models in health care systems. NEJM Catalyst, 5(9), CAT.24.0131.
[7] World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization.
[8] Reddy S, Rogers W, Makinen VP, et al. (2021). Evaluation framework to guide implementation of AI systems into healthcare settings. PLOS Digital Health, 1(2), e0000021.
[9] Jabbour S, Fouhey D, Shepard S, et al. Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study. JAMA, 15, 1776.
[10] California Telehealth Resource Center. (2024). Health Care Artificial Intelligence (AI) Toolkit, Version 2.0. Sacramento, CA: CalTRC.

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