Introduction
The use of Artificial Intelligence (AI) in healthcare promises unprecedented advancements in health technology assessment (HTA). HTA is a multidisciplinary and systematic process that methodically evaluates the properties, effects, impacts, and overall value of healthcare technologies at various stages of their lifecycle. The aim of HTA is to inform policy and decision-making in health systems, ensuring equitable, efficient, and high-quality health care.Citation1,Citation2 AI will unlock improvements in efficiency and accuracy in HTA, and patient care strategies,Citation3–6 which go beyond mere technological advancement. It includes the possibility to deepen our understanding of complex health economic issues, refine outcomes research methodologies, and elevate HTA standards.Citation7,Citation8 The use of AI in healthcare comes with ethical, legal and social challenges that can be navigated through robust and adaptable governance models.Citation9–11 Realizing the potential of AI depends on the adoption of governance models towards ethical use of AI.
This editorial seeks to elaborate on governance models to enable AI’s effective deployment in HTA. Here we emphasize the necessity for governance models that are effective and ethically sound, aligning them with the principles of equity and innovation.Citation12,Citation13 We’re guided by a vision where AI’s impact on healthcare decision-making is both transformative and ethically grounded,Citation14 through (i) building awareness of current global governance approaches of AI in healthcare, (ii) extracting governance lessons from Big Data in healthcare, and (iii) considering the challenges and gaps in AI governance. The crucial question at the heart of this evolution is how to achieve a balance between innovation and ethics in the governance of AI for HTA.
Global governance approaches of AI in healthcare
The United Nations (UN) and the World Health Organization (WHO) have played crucial roles in shaping governance models for AI, with a focus on enhancing healthcare decision-making processes, integrating AI technologies, and addressing the needs of a rapidly evolving healthcare landscape. The UN’s recommendation for AI governance seeks to align AI development with international norms.Citation15 It highlights five principles as preliminary recommendations, set to be finalized by August 2024 (). The recent UN General Assembly resolution A/78/L.49, adopted on 11 March 2024, operationalizes the UN’s commitment to leveraging AI for sustainable development while ensuring safety, security, and trustworthiness.Citation16 This resolution emphasizes effective governance, and the alignment of AI systems with human rights, reinforcing the UN's guiding principles on AI governance. Similarly, the WHO presented guidance on the ethics and governance of AI in healthcare as recommendations for governments, technology companies, and healthcare providers to ensure the appropriate use of AI.Citation17 This guidance emphasizes AI's potential to significantly improve healthcare outcomes and was recently applied in the context of Large Multi-Modal Models (LMMs).Citation18
While both organizations emphasize inclusivity and public interest (), there are principles that do not share exact similarities. For instance, the UN references important international normative frameworks to guide the design of AI governance models, such as the UN Charter and International Human Rights. The WHO emphasizes a more ethical interpretation of accountability. These differences highlight potential gaps and inconsistencies but also demonstrate various models in framing guiding governance principles for AI use. A nuanced approach to governance models can help bridge these gaps.Citation19 To this end, the governance of Big Data in healthcare may inform the deployment of AI in healthcare, particularly in HTA.
Governance lessons from Big Data in healthcare
The historical application of governance principles in healthcare and Big Data has revealed critical insights into ethical considerations, data privacy, and regulatory frameworks.Citation17,Citation18 With the shift to electronic health records (EHR) and the advent of Big Data analytics, issues like patient consent, data security, and algorithmic biases have become prominent. These developments have highlighted the need for robust governance models that can adapt to technological advancements while safeguarding patient rights and ensuring equitable access to healthcare.Citation20,Citation21
The potential of Big Data in the health sector is significant, offering enhancements in personal and clinical care, public health, and research through the integration and analysis of large healthcare datasets and EHR.Citation22 However, this potential also brings forth privacy, security, and ethical challenges that necessitate ongoing ethical oversight and comprehensive governance frameworks that evolve in response to these issues.Citation22,Citation23 The European Union’s General Data Protection Regulation (GDPR) exemplifies efforts to strengthen data protection and privacy, underscoring the importance of regulatory responses to the complexities introduced by Big Data in healthcare.Citation22 Balancing privacy concerns with the benefits of Big Data in healthcare is intricate, as governance frameworks strive to protect privacy without stifling innovation.Citation21 This balance is crucial in navigating the legal and ethical dilemmas associated with patient privacy.
Big Data in healthcare has emphasized the need for coordinated policy development to tackle issues of data quality, confidentiality, usage, and interoperability.Citation24,Citation25 Consequently, integrative, and increasingly democratic approaches have enhanced interoperability, data quality, and governance. These initiatives lay the groundwork for addressing both shared and unique challenges associated with the use of AI in HTA.
Challenges and gaps in the governance of AI in HTA
Incorporating AI into HTA offers potential solutions, particularly in analyzing complex relationships within Big Data, such as voluminous EHR and real-world evidence (RWE).Citation24,Citation25 AI can expedite decision-making processes and promote fairness,Citation26 while enhancing disease or risk detection and healthcare classification. It aids in identifying patient cohorts with shared characteristics that may not be readily discernible through conventional methods. For example, AI tools have been used to select patients for oncology studies or extract physician-reported side effects from EHR.Citation26 Furthermore, AI can accelerate time-consuming tasks such as systematic literature reviews, assist with tailoring treatment options such as personalized treatment, and predict trajectories of health outcomes.Citation3,Citation26 Challenges in the application of AI exists in the form of several aspects such as the lack of longitudinal data, especially for advanced therapeutic medicinal products (ATMPs), lack of high-quality data sources, combing clinical data with RWE, and barriers to perform complex health interventions.Citation25,Citation26
The fusion of healthcare and technology is primarily characterized by the integration of various types of data and advancements in technology—transforming decision-making through AI-augmented systems.Citation6,Citation13,Citation26 When AI is incorporated into HTA, it should be designed to augment, not replace, human intelligence. A human-centered, problem-driven approach to building AI systems involves considerations of design, development, evaluation, validation, scaling, diffusion, monitoring, and maintenance.Citation6 This approach requires the involvement of stakeholders who understand the core principles of ethical AI use and can evaluate the effectiveness of outcomes.Citation3,Citation6
Monitoring the use of AI in HTA will involve aligning with the recommendations for governance frameworks.Citation15,Citation18,Citation27 Establishing global governance guidelines for AI in HTA is crucial, covering areas such as data management, privacy, ownership, and independent decision-making.Citation3,Citation13,Citation26 Operationalizing the governance principles from the UN and WHO could be a significant step towards refining a governance framework for AI use in HTA. To achieve this goal, provides essential questions aimed at assisting HTA professionals in exploring how the governance of AI can be ethically structured and fully realize its potential.
Conclusion
Integrating AI into HTA models requires a forward-looking approach that balances innovation with ethical considerations. Insights from global governance models, past applications, and contemporary challenges inform the development of comprehensive, adaptable governance frameworks. Moreover, the essential questions on the governance of AI in HTA represent an applied approach to operationalize the principles of the UN and WHO. However, international cooperation and dialogue will be crucial in ensuring that AI acts as a catalyst for improving patient outcomes and bridging health disparities. This journey will require concerted efforts from all stakeholders toward flexible and adaptable regulatory frameworks that accommodate the unique elements of AI and enhance its utility in healthcare.
Transparency
Declaration of financial/other interests
JLC, DB, and RC are employees at Syenza and consult for pharmaceutical, medical device, and healthtech companies.
Author contributions
JLC, DB, and RC: Study concept and design, data collection, data interpretation, manuscript writing, and manuscript revisions.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Acknowledgements
Sumona Bose, former Syenza associate, for contributing to prior drafts of the manuscript.
Additional information
Funding
References
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