1. Introduction
Artificial intelligence (AI) is reshaping the global healthcare landscape, offering transformative opportunities for diagnostics, treatment, and patient care. The rapid growth of AI technologies, fuelled by advancements in machine learning (ML), digitized health data, and computational power, has enabled breakthroughs in areas traditionally dominated by human expertise [
1,
2,
3,
4]. This article presents a structured synthesis of the relevant literature, integrating insights from multiple disciplines, including healthcare, ethics, policy, and AI governance. Recognizing that achieving reliable AI is inherently interdisciplinary, we explicitly incorporate perspectives from law, sociology, computer science, and public health to offer a holistic analysis of AI’s implications in healthcare. Our discussion outlines how interdisciplinary collaboration among ethicists, clinicians, AI engineers, legal experts, and policymakers can shape AI governance and mitigate risks. To ensure a comprehensive perspective, sources were selected using a targeted review process, prioritizing peer-reviewed journal articles in PubMed, Scopus, and Web of Science published within the last decade. The selection criteria focused on studies that critically evaluate AI’s ethical, regulatory, and implementation challenges, allowing us to present a holistic view of the field.
From predicting disease trajectories to optimizing resource allocation, AI has the potential to address long-standing challenges in healthcare, enhancing precision, efficiency, and accessibility [
5,
6,
7,
8]. However, the existing literature often examines AI’s technological advancements in isolation, overlooking the intersection of ethical risks, regulatory gaps, and real-world implementation challenges. This fragmented approach creates a critical research gap, as there is a lack of comprehensive, multidisciplinary frameworks that integrate AI ethics, governance, and practical deployment strategies in healthcare. Furthermore, different categories of AI systems vary in their interpretability, risk profiles, and suitability for healthcare applications [
9]. While traditional rule-based AI models and decision trees offer greater transparency, deep learning-based AI—especially neural networks—often lacks interpretability but outperforms simpler models in tasks such as medical image analysis and genomics. Explainable AI (XAI) techniques are emerging as a bridge, seeking to balance accuracy and interpretability in clinical applications [
10,
11]. However, there is still a pressing need to assess whether certain AI categories provide inherent advantages over others in terms of safety, accountability, and regulatory feasibility.
In addition to ethical and legal concerns, AI-driven healthcare transformations have profound social consequences. The accessibility of AI-powered medical interventions raises concerns about the digital divide—patients in low-income regions or marginalized communities may face disproportionate barriers to benefiting from AI-assisted healthcare. Limited digital literacy, infrastructure deficits, and algorithmic biases can reinforce existing health disparities rather than alleviate them [
12]. Furthermore, the automation of certain healthcare processes raises questions about the displacement of healthcare workers, requiring strategic workforce adaptation to ensure AI complements rather than replaces human expertise. The societal impact of AI in healthcare extends to patient autonomy, where algorithmic decision-making could influence treatment pathways in ways that may challenge shared decision-making models between patients and physicians. Without careful oversight, AI-driven clinical tools could exacerbate power imbalances, particularly among populations with limited healthcare agencies or lower health literacy [
13]. This study addresses this gap by providing a cross-disciplinary perspective on AI governance, offering concrete recommendations for ensuring transparency, fairness, and sustainability in AI-driven healthcare systems. As AI continues to evolve, bridging these gaps is essential to maximizing its benefits while mitigating ethical, legal, and societal risks.
1.1. The Promise and Risks of AI in Healthcare
AI’s expansion into healthcare presents unique risks that necessitate robust ethical frameworks. Patient safety, data privacy, and regulatory uncertainty are major concerns. In clinical applications, AI models often function as opaque “
black boxes”, making it difficult for healthcare professionals to interpret or trust their decision-making processes [
14,
15,
16]. For example, convolutional neural networks (CNNs) used in medical imaging, such as AI-powered radiology tools for detecting tumors in MRI scans, often lack clear explanations for their predictions, making it difficult for healthcare professionals to interpret or trust their decision-making processes [
17,
18]. Similarly, AI-driven sepsis prediction models in intensive care units have demonstrated high accuracy but provide little insight into the reasoning behind their risk assessments, leading to concerns over clinical adoption [
19].
In selecting the references for this section, we prioritized studies that examine real-world challenges in AI governance, data privacy, and the limitations of current ethical frameworks. We also included policy analyses that assess gaps in existing regulations and explore potential governance solutions. This opacity not only hinders trust but also raises concerns about accountability when errors occur. A key issue is the need for explainable and interpretable AI models. Without transparency, AI decisions may undermine clinician trust and accountability [
20,
21,
22]. Additionally, AI applications rely heavily on vast amounts of patient data, creating privacy concerns and exposing vulnerabilities to cyberattacks [
23]. The ethical use of such sensitive information requires adherence to principles of non-maleficence and beneficence, aligning with the core tenets of medical ethics. Regulatory frameworks often lag behind technological advancements, leading to a fragmented landscape that varies across regions, further complicating the adoption of AI in healthcare [
24,
25]. These regulatory gaps emphasize the importance of establishing harmonized global standards to ensure that AI technologies are deployed responsibly and equitably.
Beyond these ethical and regulatory challenges, AI in healthcare also introduces significant social implications. The integration of AI into healthcare workflows may lead to shifts in physician-patient interactions, altering traditional models of care delivery. As AI systems become more prevalent in diagnostics and treatment planning, the human element of medical decision-making could be diminished, affecting patient experiences and trust in healthcare providers. Additionally, the employment landscape in healthcare is evolving due to automation, necessitating new training initiatives to prepare healthcare workers for AI-enhanced roles rather than complete job displacement [
26]. These shifts require careful policymaking to ensure AI adoption enhances rather than disrupts healthcare accessibility, workforce stability, and equitable patient outcomes.
1.2. Instances of AI Misuse in Healthcare and Their Repercussions
The misuse of AI, especially for generative AI tools such as ChatGPT 4o or o1 versions, in healthcare, poses significant risks, including falsification of medical records, misinformation, algorithmic bias, and privacy violations.
Fabrication of Medical Records: While synthetic data generation aids AI training, the introduction of falsified medical records into clinical workflows or fraudulent claims can mislead clinicians and distort research outcomes [
27].
Misinformation in Medical Advice: AI-generated medical guidance can be erroneous, as seen in studies where chatbots provided incorrect cancer treatment recommendations with fabricated sources, endangering patient safety [
28].
Algorithmic Bias Amplification: AI models trained on non-representative datasets have perpetuated disparities, such as dermatological AI misdiagnosing conditions in patients with darker skin tones because of biased training data [
29].
Deepfake Medical Content: AI-generated videos and images have been exploited to spread false health information, such as vaccine misinformation, fueling public distrust and hesitancy [
30].
The improper use of generative AI undermines patient trust, compromises clinical decision-making, and creates legal liabilities. Cybersecurity risks, including identity fraud and AI-powered phishing attacks, further threaten healthcare systems. Addressing these challenges requires stringent regulatory oversight, routine AI auditing, and interdisciplinary collaboration among AI developers, healthcare professionals, and policymakers to ensure ethical and responsible deployment [
31].
1.3. Defining Trustworthy AI
Ensuring the ethical deployment of AI in healthcare is not just about mitigating risks but also about establishing a foundation of trust. This leads us to the concept of Trustworthy AI, a fundamental pillar for its successful integration into healthcare, as illustrated in
Figure 1. This concept encompasses key principles such as transparency, accountability, fairness, and patient autonomy [
16,
32]. Our review included empirical studies evaluating how AI models incorporate these principles and regulatory reports outlining best practices:
Transparency ensures AI decisions are comprehensible.
Accountability requires developers and users to be responsible for outcomes.
Fairness addresses bias in AI algorithms, ensuring equitable healthcare access.
Patient autonomy ensures AI empowers patients rather than limiting decision-making.
Beyond these core principles, other essential dimensions, such as safety, security, and robustness, must also be considered. Safety ensures AI-driven systems prioritize patient well-being and do not introduce new risks in clinical settings [
33]. Security is crucial in preventing cyber threats and protecting sensitive health data from unauthorized access or manipulation [
34]. Robustness refers to an AI system’s ability to perform reliably across diverse populations and medical conditions, avoiding failures due to adversarial attacks or unforeseen data variations [
35].
Achieving trustworthy AI is not just a technical issue—it demands collaboration among engineers, ethicists, healthcare providers, and policymakers to address these additional dimensions and ensure AI systems function reliably under all conditions [
36,
37]. This interdisciplinary approach is necessary to balance technological feasibility with ethical integrity, regulatory compliance, and clinical applicability. For instance, integrating explainability mechanisms in AI models requires input from both computer scientists and healthcare professionals to ensure usability in medical decision-making. Importantly, AI must be designed to operate within socially responsible frameworks, ensuring it benefits diverse populations rather than exacerbating existing health disparities. The risk of algorithmic bias disproportionately affecting minority or low-income communities remains a pressing issue, requiring proactive bias mitigation strategies at the design stage to prevent inequitable healthcare outcomes while maintaining security, safety, and resilience in real-world settings [
5].
1.4. AI as an Ecosystem
AI does not function in isolation—it operates within a complex healthcare ecosystem, integrating into clinical workflows, data infrastructures, and regulatory environments [
23]. An AI ecosystem perspective highlights the importance of interoperability, data standardization, and collaboration among stakeholders to maximize the potential of AI technologies. Our literature selection prioritized studies on AI interoperability, data governance, and human–AI interaction. Moreover, the dynamic interplay between AI systems and human users—whether clinicians, patients, or administrators—demands a focus on human–AI interaction to ensure that these systems enhance rather than undermine healthcare delivery [
2,
14].
The regulatory landscape is a critical component of this ecosystem. Emerging policies, such as the European Union (EU)’s Artificial Intelligence Act (AIA), aim to establish stringent requirements for high-risk AI systems, including those used in healthcare [
38,
39]. These regulations emphasize the need for transparency, risk management, and compliance mechanisms to safeguard patient safety and public trust [
32]. However, the global diversity in regulatory approaches presents challenges for developers and healthcare institutions operating across borders, necessitating international cooperation and harmonization—a key theme in our proposed Regulatory Genome framework.
Additionally, the social and ethical implications of AI as an integrated ecosystem must be accounted for. The rise of AI-driven digital health platforms and telemedicine solutions has reshaped traditional patient care models, raising concerns about digital exclusion for elderly patients, rural populations, and those with limited technological literacy. Ensuring AI-driven healthcare remains equitable and accessible to all patients—regardless of socioeconomic status—requires targeted interventions, regulatory oversight, and ethical design principles [
40,
41]. This ecosystem approach informs our later discussion on bias mitigation, fairness, and sustainability, reinforcing the need for structured accountability mechanisms that extend beyond individual AI models to the broader systems in which they operate.
1.5. Towards a Regulatory Genome
The concept of a “
regulatory genome” reflects the need for a comprehensive and adaptive framework that governs the development, deployment, and monitoring of AI systems in healthcare. This framework should integrate ethical principles, technical standards, and legal requirements, ensuring that AI technologies align with societal goals and healthcare priorities [
24]. Key components include:
The regulatory genome also aligns AI development with the United Nations Sustainable Development Goals (SDGs) by proposing concrete assessment and implementation methods to ensure AI-driven healthcare solutions promote equity and accessibility [
24,
37]. For instance, AI’s role in predictive disease modeling can enhance early intervention strategies, reducing healthcare costs while improving patient outcomes. However, if not properly governed, AI innovations may contribute to systemic inequities, reinforcing existing disparities rather than reducing them.
To move beyond a theoretical construct, we introduce a hypothetical case study demonstrating the practical implementation of the regulatory genome in healthcare:
Case Study: AI-Driven Early Detection of Diabetic Retinopathy in Low-Resource Settings A global health initiative deploys an AI-powered diabetic retinopathy screening system in rural areas where access to ophthalmologists is limited. The regulatory genome framework can enable that:
Transparency: The AI model’s decision-making process is explainable through heatmaps and confidence scores, helping clinicians validate its outputs.
Bias Mitigation: The model can undergo algorithmic fairness audits, ensuring it does not disproportionately misdiagnose patients from underrepresented ethnic groups.
Compliance: The AI system should adhere to the EU AI Act’s high-risk category standards and follow data protection regulations such as GDPR, with federated learning techniques ensuring privacy-preserving AI training.
Continuous Monitoring: The system can undergo quarterly post-market surveillance audits, assessing real-world performance across diverse populations.
Stakeholder Involvement: Regulators, clinicians, and local community representatives contribute to policy adjustments, ensuring culturally sensitive AI deployment.
By operationalizing this adaptive model, the regulatory genome helps keep AI in healthcare transparent, fair, and responsive to changing societal needs.
1.6. Translating the AI Ecosystem Perspective into Actionable Policy and Governance Strategies
To effectively assess and implement SDG alignment, we propose the following key methods:
Impact Assessment Metrics: Developing standardized AI impact indicators aligned with specific SDGs, such as healthcare accessibility (SDG 3), reduced inequalities (SDG 10), and responsible innovation (SDG 9). A study by Vinuesa et al. (2020) provides a comprehensive review of how AI can act as an enabler across various SDG targets, highlighting the importance of creating metrics to assess AI’s contributions to these goals [
24].
AI Governance Audits: Requiring AI systems in healthcare to undergo periodic audits to evaluate their contributions to sustainability, ethical compliance, and fairness [
42].
Regulatory Incentives: Encouraging AI developers and healthcare institutions to adopt SDG-aligned AI practices through regulatory incentives, such as funding opportunities and compliance certifications [
43].
Stakeholder-driven Evaluation: Engaging multidisciplinary experts, including healthcare professionals, ethicists, and policymakers, in qualitative and quantitative reviews of AI’s contributions to sustainable healthcare ecosystems. A systematic literature review by Birkstedt et al. (2023) emphasizes the importance of involving diverse stakeholders in AI governance to ensure comprehensive evaluation and accountability [
44].
While the promise of AI in healthcare is undeniable, the preceding analysis highlights critical gaps in addressing its ethical, regulatory, and practical challenges. The current literature often emphasizes either the technological advancements or the ethical principles in isolation, leaving a significant void in actionable frameworks that integrate these aspects comprehensively. This paper addresses that gap by offering a unified approach to understanding and implementing trustworthy AI in healthcare. Our focus stems from the urgent need to navigate the complexities of privacy, bias, regulatory ambiguity, and patient autonomy, which remain underexplored in their interplay with real-world clinical perspectives. By bridging theoretical principles with practical implementation, this paper provides measurable frameworks and policy recommendations tailored to the evolving healthcare ecosystem. Moreover, it emphasizes emerging challenges such as adversarial resilience and sustainable AI development, areas that are critical but insufficiently discussed in the existing literature. For example, adversarial attacks can manipulate medical AI systems by subtly altering input data, leading to incorrect diagnoses or treatment recommendations [
45]. Addressing this requires developing robust algorithms capable of detecting and mitigating such attacks. Additionally, sustainable AI development involves creating models that are not only effective but also environmentally responsible, considering factors such as energy consumption and resource utilization [
46]. This paper not only advances academic discourse but also equips stakeholders with the tools to ensure ethical AI integration, fostering improved patient outcomes and equitable healthcare delivery.
1.7. Key Contributions of This Study
This paper provides a structured, interdisciplinary framework that bridges AI ethics, governance, and real-world deployment strategies in healthcare. The main contributions are as follows:
A Unified AI Governance Framework: Unlike existing approaches that focus separately on AI ethics or regulation, this study integrates transparency, fairness, accountability, and sustainability into a cohesive governance structure.
Quantifiable AI Trustworthiness Metrics: We introduce measurable indicators—such as explainability scores, bias reduction rates, compliance metrics, and sustainability benchmarks—to evaluate AI systems in healthcare.
Comparative Analysis of AI Categories: We assess symbolic AI, ML, and hybrid AI approaches, identifying their strengths, risks, and suitability for clinical applications.
Regulatory Genome Concept for AI Oversight: We introduce an adaptive regulatory framework that aligns with global AI governance trends and SDGs, ensuring ethical AI deployment.
Bias Mitigation and Algorithmic Fairness Strategies: We provide a roadmap for diverse data curation, algorithmic fairness audits, and continuous monitoring to reduce disparities in AI-driven healthcare.
Interdisciplinary Policy Recommendations: We offer actionable guidelines for policymakers, healthcare institutions, and AI developers, ensuring compliance with emerging regulations such as the EU AIA and Food and Drug Administration (FDA)’s Software as a Medical Device (SaMD) framework.
By synthesizing these elements, this study bridges the gap between high-level AI ethics and practical implementation, contributing to the development of trustworthy, equitable, and sustainable AI in healthcare.
3. From Principles to Practice: Defining and Evaluating Trustworthy AI
As AI’s role in healthcare expands, moving from high-level ethical principles to concrete implementation strategies becomes an urgent priority. Robust frameworks that ensure transparency, explainability, and accountability are essential for cultivating trust in AI-driven medical systems. Beyond outlining abstract ideals, the healthcare industry and regulators must identify verifiable metrics and practical techniques to assess whether AI models operate fairly, accurately, and safely in clinical environments.
AI systems vary significantly in their ability to support trustworthy implementation in healthcare. Symbolic AI and rule-based expert systems provide greater explainability but often struggle with complexity in healthcare [
76]. ML models, particularly deep learning architectures, offer superior predictive power but suffer from interpretability challenges, making them less transparent [
77]. Meanwhile, hybrid AI approaches that integrate symbolic reasoning with deep learning have gained attention for their ability to balance transparency and predictive performance, for example, for targeted metabolomics analysis of diabetic retinopathy [
78]. Identifying the most suitable AI category for specific clinical tasks is a crucial step toward ensuring trust and accountability in healthcare AI applications.
Figure 2 provides a conceptual framework for achieving Trustworthy AI by balancing helpful and harmful factors that influence AI integration and its impact on outcomes. The framework is structured along two axes:
Ease of Integration (horizontal axis)—This axis represents the balance between helpful internal attributes that improve AI adoption and harmful external factors that create barriers, such as regulatory gaps and privacy risks. AI systems that emphasize fairness, explainability, and ethical governance tend to integrate more smoothly, while those facing compliance challenges or regulatory misalignment may struggle with adoption.
Impact on Outcomes (vertical axis)—This axis differentiates internal organizational efforts (bottom) from external regulatory, ethical, and societal constraints (top). A higher impact on outcomes is observed when AI systems align with compliance and security needs, whereas a lower impact may arise from transparency and explainability issues that require internal efforts to improve AI adoption.
Each quadrant represents a critical factor influencing Trustworthy AI development:
Top-left (Fair, Accountable, and Bias-Free AI): Highlights fairness, accountability, and equity in AI design, ensuring responsible decision-making and reducing biases in healthcare AI models.
Top-right (Secure, Compliant, and Privacy-Focused AI): Represents the importance of regulatory adherence and cybersecurity measures, ensuring compliance but also presenting challenges if excessive constraints hinder innovation.
Bottom-left (Transparent, Reliable, and Explainable AI): Focuses on AI systems that prioritize transparency and interpretability, enabling healthcare professionals to understand and trust AI-generated decisions.
Bottom-right (Sustainable, Scalable, and Trustworthy AI): Emphasizes long-term viability, ethical scalability, and the alignment of AI deployment with sustainability and equity goals.
Applying this framework to specific AI categories can help stakeholders assess which models best fit different healthcare applications. For example, while deep learning may be more effective for complex imaging tasks such as tumor detection, rule-based systems might be preferable for clinical decision support where interpretability is paramount [
79]. By aligning AI selection with clinical context, regulatory requirements, and ethical imperatives, healthcare organizations can ensure AI deployment is both effective and trustworthy [
10].
This framework also allows stakeholders to assess their AI implementations by identifying strengths and areas for improvement. An AI model that excels in privacy compliance but lacks explainability would fall in the top-right quadrant and may need refinement to improve transparency. Likewise, healthcare institutions should shift from fragmented technical solutions toward a more holistic approach that integrates fairness, accountability, and sustainability. Achieving Trustworthy AI requires a continuous process of refinement, addressing regulatory gaps, and reinforcing ethical, transparent, and scalable AI practices that benefit both healthcare providers and patients.
3.1. Transparency and Explainability: Moving Beyond the Black Box
Ensuring trustworthy AI in healthcare requires not only fairness, accountability, and privacy but also transparency and explainability. One of the major barriers to trust is the “
black-box” problem, where AI models—especially deep learning systems—lack interpretability, making it difficult for clinicians to understand their decision-making processes. This opacity can erode trust, pose safety risks, and hinder adoption in clinical practice [
80,
81,
82,
83]. To effectively enhance transparency, stakeholders must implement clear metrics and techniques that improve AI interpretability. Our analysis systematically selected empirical studies, regulatory frameworks, and industry guidelines from PubMed, Scopus, and Web of Science to evaluate the effectiveness of existing explainability methods in healthcare AI. Referring back to the conceptual framework shown in
Figure 2, transparency and explainability are essential internal attributes that move AI systems closer to trustworthiness. AI developers should focus on interpretable model architectures, post-hoc explainability techniques, and user-friendly decision-support tools that bridge the gap between complex algorithms and clinical applicability. When organizations prioritize transparency initiatives, they move closer to the center of
Figure 2, balancing trust-building attributes against the challenges of real-world integration.
3.1.1. Proposing Measurable Metrics for Transparency in AI Systems
To advance beyond abstract principles, measurable indicators of transparency must be identified. Such metrics might include the percentage of model outputs accompanied by interpretable explanations, the level of detail these explanations provide, or standardized “
explainability scores” that gauge how easily a human can comprehend the reasoning behind an AI’s decision [
16,
83,
84]. Implementing explainability scores involves several approaches:
Feature Contribution Analysis: Techniques such as SHapley Additive exPlanations (SHAP) assign an importance value to each input feature, indicating its contribution to the model’s output [
85]. For instance, in a clinical decision support system predicting patient outcomes, SHAP values can highlight which clinical parameters (e.g.
, blood pressure, cholesterol levels) most significantly influenced a specific prediction. This allows healthcare professionals to understand the model’s reasoning and assess its alignment with clinical knowledge [
11].
Local Interpretable Model-agnostic Explanations (LIME): LIME approximates the AI model locally with an interpretable model to explain individual predictions [
86]. In practice, if an AI system recommends a particular treatment plan, LIME can provide a simplified explanation of that specific recommendation, enabling clinicians to evaluate its validity before implementation [
11].
Attention Mechanisms in Neural Networks: In models such as attention-based Bidirectional Long Short-Term Memory networks, attention weights can serve as proxies for feature importance, offering insights into which aspects of these input data the model focuses on during prediction. For example, in patient monitoring systems, attention mechanisms can help identify critical time periods or vital signs that the model deems most relevant, thereby providing transparency in its decision-making process [
87].
In clinical contexts, these could be mapped to real-world performance measures—such as the rate at which physicians can accurately identify a model’s reasoning errors or the time it takes for healthcare staff to understand and trust an algorithm’s recommended treatment option [
23]. Setting these measurable goals encourages iterative improvement, aligning with the idea of AI as an evolving ecosystem that thrives on continuous feedback and regulatory oversight. In terms of the framework in
Figure 2, establishing metrics for transparency can help healthcare organizations move from a purely aspirational stance to a more outcomes-focused approach. By systematically assessing transparency, they ensure that this dimension of AI trustworthiness is not just a guiding principle but a quantifiable target.
Another promising approach involves developing structured documentation protocols—akin to model “nutrition labels”—that summarize the model’s data sources, training methodology, known limitations, and prior performance across diverse patient populations [
82]. By employing standardized reporting frameworks, stakeholders can more consistently evaluate the transparency and trustworthiness of AI implementations, thereby reinforcing the beneficial factors that drive the healthcare ecosystem toward trustworthy AI.
3.1.2. Advancements in Explainability Techniques
In parallel, the technical community has made considerable progress in rendering complex models more comprehensible. Model-agnostic techniques such as LIME and SHAP can be applied to a wide range of black-box models, highlighting the most influential features that drive particular predictions [
80,
88]. These methods help clinicians understand why an AI tool suggests a particular diagnosis or treatment, enabling them to identify potential misjudgments, ensure consistency with medical knowledge, and communicate the rationale to patients.
Other efforts focus on building transparency into the model design itself. Intrinsically interpretable models—such as decision trees or rule-based systems—prioritize understandability from the outset, albeit sometimes at the expense of accuracy. Hybrid solutions seek to bridge this gap by pairing high-performing black-box models with interpretable surrogate models or post-hoc explanation layers that maintain performance while offering human-readable logic [
71,
82]. Neural networks, historically challenging to interpret, now benefit from visualization methods and layered explanations that reveal which inputs strongly influence a network’s decisions [
89]. These visualizations, especially for text-based models, can be integrated into clinical workflows, allowing clinicians to trace how different textual inputs—such as patient history or lab values—affect model outputs [
90,
91].
3.1.3. Building Trust Through Understanding and Shared Accountability
Combining robust explainability techniques with clear performance metrics not only aids healthcare practitioners in making informed decisions but also supports regulators in assessing compliance with emerging guidelines that call for AI transparency. From the perspective of “
towards regulatory genomes”, scalable explainability standards ensure that trust-building measures are not ad hoc but systematically embedded into the lifecycle of AI development and deployment [
92]. Ultimately, as AI systems evolve, their transparency and comprehensibility must evolve in tandem, ensuring that high-stakes healthcare decisions are guided by algorithms that can justify their reasoning, adapt to diverse patient populations, and maintain trust across the entire healthcare ecosystem.
In selecting references for this section, we provided a balance between technical solutions, regulatory considerations, and case studies. This approach allowed us to provide a holistic perspective on how transparency can be operationalized in healthcare AI. As illustrated in
Figure 2, every step—establishing measurable transparency goals, adopting cutting-edge explainability methods, and enforcing accountability—guides healthcare AI closer to the central goal of achieving
Trustworthy AI. In this process, internal organizational efforts bolster reliability and fairness while managing potentially harmful external influences such as regulatory gaps or privacy risks. The result is a more resilient, ethically grounded, and equitable healthcare AI ecosystem, capable of adapting its transparency and comprehensibility to diverse patient populations and changing clinical contexts.
3.2. Accountability in AI Decision-Making
As AI systems increasingly influence clinical judgments and patient outcomes, the need for clear accountability frameworks becomes paramount. Unlike traditional medical tools, AI-driven diagnostic and therapeutic recommendations can arise from complex, opaque models that challenge existing legal and ethical paradigms. Who bears responsibility if an autonomous surgical robot makes an error or if an ML algorithm systematically disadvantages a particular patient population? These questions demand rigorous standards, robust governance models, and transparent lines of accountability to maintain trust and integrity in healthcare AI.
In the domain of autonomous robotic surgery, for example, a structured approach to accountability must distinguish between accountability, liability, and culpability (see
Table 3). While “
accountability” relates to the obligation of an entity—be it a developer, healthcare provider, or institution—to answer for the performance of AI-driven tools, “
liability” involves potential legal consequences and compensation for the harm caused. “
Culpability” addresses moral wrongdoing, which is challenging to apply to non-human agents. O’Sullivan and colleagues highlight how future surgical robots capable of performing routine tasks under human supervision could require new frameworks to apportion responsibility and uphold patient safety [
93]. In this scenario, the concept of a “doctor-in-the-loop” ensures that clinicians retain ultimate control and ethical oversight, bridging current clinical responsibilities with the emerging capabilities of autonomous systems.
Beyond clinical interventions, accountability in AI healthcare systems also depends on embedding ethical principles into practical governance structures. As Shneiderman suggests, multi-level strategies are essential for ensuring reliable, safe, and trustworthy AI [
94]. At the team level, adherence to rigorous software engineering practices—such as documentation, traceability, and thorough verification—can facilitate post-hoc analyses of failures. At the organizational level, a strong safety culture promotes continuous improvement and learning from errors. At the industry and societal level, external audits, regulatory oversight, and professional standards can provide independent checks on the trustworthiness of AI. Together, these measures help clarify who is accountable when AI models perform poorly, exacerbate disparities, or cause patient harm.
From a broader policy perspective, Morley and colleagues outline how accountability is integral to ethical AI in healthcare, linking it to issues of epistemic soundness, fairness, and system-level governance [
95]. Ethical guidelines and regulatory principles must align with clinical realities, ensuring that decision-makers, regulators, and developers remain answerable for their contributions. Formalizing accountability encourages ongoing stakeholder engagement—patients, clinicians, policymakers, and developers—thereby strengthening public trust and reducing the risk of a loss of confidence that could hinder AI adoption in healthcare.
Table 3 provides a structured overview of the key dimensions of accountability in healthcare AI, outlining critical components and their implications.
4. Future Directions: A Roadmap for Trustworthy AI in Healthcare
As AI becomes increasingly integral to healthcare, meeting the demand for trustworthy, equitable, and patient-centered solutions calls for a forward-looking, strategic approach. The future depends on cultivating strong collaborations among developers, clinicians, policymakers, and ethicists, as well as fostering global standards and adaptive regulatory models. Emphasis must shift from reactive measures to proactive frameworks that anticipate emerging challenges, promote sustainability, and ensure that AI-driven care remains both ethically grounded and clinically valuable.
A key challenge is the lack of quantifiable frameworks for assessing AI governance. To address this, we propose a structured approach that integrates clear performance indicators, policy recommendations, and real-world implementation strategies. This future-oriented perspective recognizes that current ethical, regulatory, and infrastructural approaches must evolve in tandem with the technologies they govern. Transparent performance metrics, bias mitigation strategies, and robust oversight mechanisms must become the norm. Moreover, we must expand our understanding of AI’s long-term implications—both environmental and socioeconomic—ensuring that breakthroughs in precision medicine do not come at the expense of sustainability, inclusivity, or public trust.
Table 4 highlights a forward-looking, holistic approach to AI governance in healthcare.
4.1. Cross-Disciplinary Collaboration for AI Governance
Ensuring that AI systems are both clinically effective and ethically sound requires comprehensive governance frameworks developed through cross-disciplinary collaboration. This study adopts an interdisciplinary perspective, drawing upon AI ethics, legal frameworks, clinical practice, and regulatory science to propose actionable recommendations. For instance, regulatory adaptation strategies must incorporate insights from medical professionals to ensure that AI compliance mechanisms do not hinder clinical efficiency, while ethicists and technologists contribute to the formulation of fairness and bias mitigation techniques. AI developers, healthcare practitioners, policymakers, and ethicists each bring unique perspectives and expertise to the table, shaping AI policies and standards that reflect the realities of clinical care, technological limitations, societal values, and regulatory imperatives [
96,
97,
98]. Such cross-disciplinary dialogue is essential for anticipating potential ethical dilemmas, addressing biases, and ensuring that AI tools enhance patient care without inadvertently harming underserved communities.
The complexity of healthcare settings, coupled with the rapid evolution of AI technologies, means that no single stakeholder can grasp all the implications. Policymakers and public sector administrators provide insights into regulatory structures and enforcement mechanisms that maintain accountability and fairness. Healthcare professionals contribute to the clinical context, ensuring that AI-driven solutions align with patient needs and integrate seamlessly into existing workflows. Ethicists help ensure that principles such as fairness, autonomy, and transparency are not only articulated but operationalized. AI developers, for their part, translate these principles into technical architectures, develop interpretable models, and maintain robust cybersecurity measures [
96,
99,
100]. By involving all these groups from the outset, a “
design for governance” approach becomes more feasible, and the resulting AI frameworks become more pragmatic, inclusive, and sustainable.
International collaborations play a crucial role in developing global AI standards and aligning regulatory frameworks across jurisdictions. Key organizations such as the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) have jointly introduced ISO/IEC 42001:2023 [
101], the first global AI management system standard, providing a structured approach for responsible AI deployment. Similarly, the IEEE Standards Association has established IEEE 2801-2022 (IEEE Recommended Practice for the Quality Management of Datasets for Medical Artificial Intelligence), a framework for ensuring quality management of datasets used in AI-driven medical devices [
102]. These initiatives set international benchmarks that help standardize AI governance, ensuring transparency, fairness, and accountability across different regions.
Beyond technical standards, industry and government collaborations are essential for establishing consistent ethical guidelines and bridging regulatory differences [
103,
104]. For instance, the SPIRIT-AI and CONSORT-AI guidelines provide international standards for clinical trials of AI systems, ensuring complete and transparent reporting of clinical trial protocols and reports involving AI interventions [
105]. Additionally, the FUTURE-AI consortium has developed an international consensus guideline for trustworthy and deployable artificial intelligence in healthcare, addressing technical, clinical, ethical, and legal considerations [
106].
Industry and government collaborations are also essential for establishing consistent ethical guidelines and bridging regulatory differences. While government agencies provide baseline policies, global consortia, professional organizations, and private sector alliances refine these into practical, scalable governance models. These partnerships facilitate knowledge exchange, accelerate policy development, and ensure AI regulations align with the United Nations SDGs. As AI becomes more embedded in healthcare, governance must evolve just as rapidly. Effective AI governance requires an interdisciplinary approach, bringing together technologists, clinicians, policymakers, and ethicists to create a healthcare AI ecosystem that is reliable, equitable, and patient-centered [
107]. By fostering international collaboration, stakeholders can drive responsible AI adoption, ensuring healthcare AI solutions are ethically sound, technically robust, and globally harmonized.
4.2. Integrating AI with Ethical Frameworks: Towards “Ethical by Design” AI
Building trust in healthcare AI requires more than after-the-fact adjustments; it calls for weaving ethical considerations directly into the fabric of AI systems from the start. An “
Ethical by Design” approach ensures that core principles—such as fairness, safety, privacy, and accountability—are not retrofitted but form the foundation of an AI system’s architecture, algorithms, and operational protocols [
108,
109,
110]. Rather than viewing ethics as an external checkpoint, developers, clinicians, and policymakers should treat it as a guiding compass that informs technical decisions, clinical integration, and policy formation.
In this model, developers collaborate closely with ethicists, legal experts, and healthcare professionals to anticipate potential harm or biases early in the design process. Techniques from responsible AI frameworks, such as continuous impact assessments, formalized documentation, and user-focused explainability tools, support proactive decision-making [
93,
108,
109]. For example, a surgical robot could be engineered with clearly defined accountability layers and operational constraints that safeguard patients, ensuring a human surgeon remains “in the loop” for critical decisions [
93]. Similarly, AI-driven diagnostic tools can be designed with embedded fairness checks and transparency features, enabling clinicians and patients to understand how predictions are made and to identify instances of bias or error.
Operationalizing “
Ethical by Design” also entails going beyond surface-level compliance. It involves implementing iterative audits, rigorous validation protocols, and clear escalation paths for addressing misconduct or errors. Ethical frameworks become living documents that adapt to evolving technology and data landscapes, thus ensuring that AI systems remain aligned with core human values as they advance [
111,
112]. Thus, this integrated approach not only strengthens trust and acceptance of AI in healthcare but also enhances clinical outcomes by making ethical integrity, patient well-being, and responsible innovation inseparable from the development and deployment lifecycle.
4.3. Sustainable and Ethical AI Development
The pursuit of trustworthy AI in healthcare requires a broader perspective that considers not only the immediate clinical benefits of AI systems but also their long-term environmental, social, and economic implications. Sustainable AI extends beyond designing algorithms for clinical accuracy and efficiency; it requires a lifecycle approach—from data sourcing and model training to deployment and decommissioning—according to principles of ecological integrity, social justice, and intergenerational equity [
46,
113]. In short, sustainable AI must both support global sustainability goals and operate in an environmentally responsible manner.
One pressing concern is the substantial energy consumption and environmental footprint associated with training and maintaining large AI models, often powered by resource-intensive data centers [
114,
115]. Healthcare organizations, already under scrutiny for their contribution to greenhouse gas emissions and environmental stressors, must weigh the ecological costs of their AI investments. Striving for “green AI” involves:
Optimizing algorithms for energy efficiency, reducing computational complexity while maintaining performance.
Leveraging renewable energy to power AI infrastructure, such as cloud providers committed to carbon neutrality.
Implementing hardware recycling strategies to reduce e-waste from outdated computing resources [
116].
To assess the environmental impact of AI systems, several tools and metrics have been developed:
Carbontracker: This tool monitors hardware power consumption and local energy carbon intensity during the training of deep learning models, providing accurate measurements and predictions of the operational carbon footprint [
117].
Energy Usage Reports: This approach emphasizes environmental awareness as part of algorithmic accountability, proposing the inclusion of energy usage reports in standard algorithmic practices to promote responsible computing in ML [
118].
By utilizing these tools, stakeholders can effectively measure and mitigate the environmental burden of AI systems. Beyond environmental sustainability, ethical sustainability requires that AI promotes equitable access to healthcare resources and does not exacerbate existing disparities [
24,
113]. AI tools must be designed and validated to avoid amplifying biases or producing harmful downstream effects, such as misinformation or discriminatory treatment paths. As AI applications increasingly influence healthcare policies and global health initiatives, close alignment with the United Nations SDGs ensures that AI-driven innovations address societal needs without compromising future generations’ ability to meet their own [
24,
35]. Therefore, sustainable AI development calls for a multi-stakeholder approach:
Policymakers should establish incentives and regulations that promote responsible innovation.
Healthcare institutions should adopt procurement policies favoring energy-efficient AI solutions and transparent supply chains.
AI developers should integrate sustainability metrics into model evaluation frameworks, ensuring that AI systems align with both ethical and environmental standards.
By combining environmental stewardship, ethical rigor, and a commitment to the long-term well-being of communities, healthcare stakeholders can ensure that AI not only enhances patient outcomes today but also preserves the ecological and moral foundations upon which future healthcare depends.
4.4. Establishing Quantifiable AI Governance Metrics
To bridge the gap between high-level ethical principles and practical implementation, we introduce a set of quantifiable governance metrics that evaluate AI systems based on transparency, bias mitigation, regulatory compliance, and sustainability. These dimensions can be measured using specific performance metrics outlined in
Table 5.
These quantifiable measures can allow AI stakeholders to systematically assess the effectiveness and ethical alignment of AI systems in healthcare. By applying this framework, organizations could track AI performance over time, improve regulatory compliance, and ensure patient safety.
4.5. Policy Recommendations for AI Governance Implementation
To operationalize these metrics, we propose the following policy-driven interventions for healthcare AI governance:
Mandatory Transparency Reporting: AI developers must publish detailed model documentation, including interpretability techniques, demographic performance disparities, and post-market monitoring results. For example, the UK NHS AI Lab mandates model transparency reports for all approved AI-driven clinical tools [
68].
Bias Mitigation Mandates: Regulators should require AI developers to conduct fairness audits and actively mitigate algorithmic biases before deployment. For example, the FDA’s SaMD regulatory framework includes demographic fairness testing in clinical AI validation [
66].
Real-Time AI Performance Audits: An AI Governance Task Force should oversee post-market AI performance, ensuring continuous validation and bias correction. For example, the EU AIA proposes AI sandboxes for continuous evaluation and refinement [
74].
Sustainability Compliance Incentives: AI developers adhering to energy efficiency standards should receive tax incentives or expedited regulatory approvals. Initiatives supporting the development and implementation of AI solutions that contribute to carbon neutrality can offer subsidies to organizations integrating sustainable AI technologies, thereby reducing their carbon footprint. Studies have shown that AI-based solutions can significantly reduce energy consumption and carbon emissions in various sectors [
116,
117,
118].
These policy interventions ensure governments and healthcare institutions enforce AI governance metrics effectively, bridging the gap between theoretical AI ethics and real-world implementation.
5. Conclusions
The integration of AI into healthcare presents both remarkable opportunities and profound responsibilities. While AI enhances diagnostic precision, treatment efficacy, and patient access, its ethical, regulatory, and societal challenges require careful governance. This study contributes by providing a structured, interdisciplinary framework that integrates AI ethics, policy, and deployment strategies, ensuring transparency, fairness, accountability, and sustainability. We introduce quantifiable trustworthiness metrics, analyze AI categories for clinical suitability, and propose the Regulatory Genome—an adaptive framework aligning AI governance with global regulatory trends and SDGs. Additionally, we outline bias mitigation strategies and offer policy recommendations to support compliance with emerging regulations such as the EU AI Act and the FDA’s SaMD framework. By synthesizing insights from healthcare, ethics, and technology policy, this study offers a comprehensive approach to responsible AI deployment, ensuring its equitable and sustainable integration into healthcare systems. To strengthen AI governance and ensure its ethical implementation, we propose the following actionable recommendations:
Regulatory Adaptation—Policymakers should develop flexible, adaptive regulations that evolve alongside AI technologies, ensuring continuous monitoring, transparency reporting, and risk assessment frameworks.
Interdisciplinary Cooperation—Collaboration among technologists, clinicians, ethicists, and policymakers is essential to establish standardized guidelines that balance innovation with ethical safeguards.
Global Policy Alignment—International cooperation should aim to harmonize AI regulations across jurisdictions, addressing disparities in privacy laws, data sovereignty, and compliance standards.
Research Priorities—Future studies should focus on empirical evaluations of AI fairness, real-world implementation of AI transparency frameworks, and the development of sustainability metrics for AI-driven healthcare.
By addressing governance gaps and proposing actionable strategies, this study contributes to ongoing discussions on ethical AI in healthcare. Moving forward, collaborative efforts between academia, industry, and regulatory bodies could be critical to maximizing AI’s benefits while mitigating risks and ensuring equitable, sustainable AI adoption.
