The SMART+ Framework for AI Systems
📝 Abstract
Artificial Intelligence (AI) systems are now an integral part of multiple industries. In clinical research, AI supports automated adverse event detection in clinical trials, patient eligibility screening for protocol enrollment, and data quality validation. Beyond healthcare, AI is transforming finance through real-time fraud detection, automated loan risk assessment, and algorithmic decision-making. Similarly, in manufacturing, AI enables predictive maintenance to reduce equipment downtime, enhances quality control through computer-vision inspection, and optimizes production workflows using real-time operational data. While these technologies enhance operational efficiency, they introduce new challenges regarding safety, accountability, and regulatory compliance. To address these concerns, we introduce the SMART+ Framework - a structured model built on the pillars of Safety, Monitoring, Accountability, Reliability, and Transparency, and further enhanced with Privacy & Security, Data Governance, Fairness & Bias, and Guardrails. SMART+ offers a practical, comprehensive approach to evaluating and governing AI systems across industries. This framework aligns with evolving mechanisms and regulatory guidance to integrate operational safeguards, oversight procedures, and strengthened privacy and governance controls. SMART+ demonstrates risk mitigation, trust-building, and compliance readiness. By enabling responsible AI adoption and ensuring auditability, SMART+ provides a robust foundation for effective AI governance in clinical research.
💡 Analysis
Artificial Intelligence (AI) systems are now an integral part of multiple industries. In clinical research, AI supports automated adverse event detection in clinical trials, patient eligibility screening for protocol enrollment, and data quality validation. Beyond healthcare, AI is transforming finance through real-time fraud detection, automated loan risk assessment, and algorithmic decision-making. Similarly, in manufacturing, AI enables predictive maintenance to reduce equipment downtime, enhances quality control through computer-vision inspection, and optimizes production workflows using real-time operational data. While these technologies enhance operational efficiency, they introduce new challenges regarding safety, accountability, and regulatory compliance. To address these concerns, we introduce the SMART+ Framework - a structured model built on the pillars of Safety, Monitoring, Accountability, Reliability, and Transparency, and further enhanced with Privacy & Security, Data Governance, Fairness & Bias, and Guardrails. SMART+ offers a practical, comprehensive approach to evaluating and governing AI systems across industries. This framework aligns with evolving mechanisms and regulatory guidance to integrate operational safeguards, oversight procedures, and strengthened privacy and governance controls. SMART+ demonstrates risk mitigation, trust-building, and compliance readiness. By enabling responsible AI adoption and ensuring auditability, SMART+ provides a robust foundation for effective AI governance in clinical research.
📄 Content
AI-driven tools and AI Systems hold significant promise across industries-enhancing diagnostics and clinical trial optimization in healthcare, detecting fraudulent transactions and automating loan risk assessments in finance, and enabling predictive maintenance and real-time workflow optimization in manufacturing. However, these technologies also carry substantial risks if not carefully designed, validated, and monitored (Bouderhem, 2024;Chustecki, 2024;De Micco et al., 2025;Ferrara et al., 2024;Khan et al., 2025;Murdoch, 2021;Panteli et al., 2025). A high-profile example is the Epic Sepsis Model, a proprietary algorithm deployed in numerous hospitals, which failed to identify 67% of patients with sepsis while generating alerts for only 18% of admissions (Habib et al., 2021;Wong et al., 2021). This poor real-world performance far below clinician judgment highlights the potential consequences of relying on AI systems without rigorous oversight. Similarly, imaging AI models trained predominantly on light-skinned patients have demonstrated worse performance for lesions on darker skin tones, leading to underdiagnosis in non-white populations (Cross et al., 2024;Rezk et al., 2022). These cases illustrate how AI systems that perform well in controlled environments can fail in practice, potentially exacerbating healthcare disparities and compromising patient safety.
While no single set of AI ethics principles has been universally agreed, let alone accepted and implemented, several trustworthy AI frameworks offer complementary guidance. The NIST AI Risk Management Framework (2023) provides a risk-based approach to AI system development, emphasizing trustworthiness across the AI lifecycle. The OECD AI Principles (2019/2024) promote innovative and responsible AI that respects human rights and democratic values. The GAO AI Accountability Framework (2021) outlines practical governance, data, performance, and monitoring practices for federal AI systems. Similarly, the EU Ethics Guidelines for Trustworthy AI identify a range of requirements that AI systems should satisfy (EU, 2019). While each framework emphasizes critical aspects of trustworthy AI, none fully addresses the unique needs of clinical research or healthcare deployment.
Building on these insights, the SMART+ Framework introduces a streamlined taxonomy tailored for healthcare AI. It integrates the core principles of Safe, Monitored, Accountable, Reliable, and Transparent (SMART), augmented with Privacy & Security, Data Governance, Fairness & Bias, and Guardrails. By doing so, SMART+ provides actionable guidance to ensure that AI systems across industries operate ethically, reliably, and safely. In the following sections, we review the relevant AI frameworks, detail each SMART+ component, and demonstrating its utility in promoting trustworthy AI adoption. We aim to provide a practical framework for achieving trustworthiness in AI systems by ensuring that all essential parameters (SMART+) are thoroughly addressed before the system is released for real-world use. Beyond deployment, our framework emphasizes the importance of continuous monitoring to maintain performance, safety, and ethical integrity over time. This holistic approach enables stakeholders to apply well-defined principles for evaluating AI systems and demonstrating their trustworthiness throughout the entire lifecycle.
The global ecosystem of AI ethics and governance is grounded in multiple international frameworks that collectively define the principles of trustworthy, safe, and human-centered AI. Among the most influential is the EU Ethics Guidelines for Trustworthy AI, which articulate seven core requirements-Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity and Fairness, Societal Well-being, and Accountability (EU, 2019). These are underpinned by four ethical principles: Respect for Human Autonomy, Prevention of Harm, Fairness, and Explicability. Together, they emphasize that AI must enhance rather than diminish human rights, with strong mechanisms for oversight, data integrity, explainability, and nondiscrimination. Complementing the EU’s perspective, the NIST AI Risk Management Framework (2023) adopts a risk-based, lifecycle-oriented approach structured around four core functions-Govern, Map, Measure, and Manage. It identifies key characteristics of trustworthy AI systems, including validity, reliability, safety, security, accountability, transparency, and fairness. Similarly, the OECD AI Principles (2019/2024) and GAO AI Accountability Framework (2021) promote transparency, human rights, and ongoing performance monitoring. The GAO’s four pillars-Governance, Data, Performance, and Monitoring-offer practical measures for organizations to maintain accountability and reliability throughout the AI lifecycle. The EU Artificial Intelligence Act operationalizes these ethical foundations into enforceable regulatory obligations by cate
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