# Le rôle de l'IA dans la recherche en santé : un examen des politiques des fondations de l'enseignement supérieur

Yuris Tri, Efendi Efendi Lod Simanjuntak Lod, Anatoliy Kostruba

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Yuris Tri, Efendi Efendi Lod Simanjuntak Lod, Anatoliy Kostruba. Le rôle de l'IA dans la recherche en santé : un examen des politiques des fondations de l'enseignement supérieur. Journal of Advanced Health Informatics Research, 2024, 2 (2), pp.102-108. 10.59247/jahir.v2i2.295. hal-05032252

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# The Role of AI in Health Research: A Policy Review of Higher Education Foundations

Yuris Tri Naili<fnref n="1" />, Efendi Lod Simanjuntak<fnref n="2" />, Anatoly Kostruba <fnref n="3" />

## ARTICLE INFO

### Article history:

Received May 18, 2024

Revised August 28, 2024

Published September 03, 2024

### Keywords:

Higher Education Policies;

AI Governance;

Regulatory Frameworks;

Ethical AI;

AI in Healthcare

## ABSTRACT

The integration of Artificial Intelligence in health research has transformed data analysis, predictive modeling, and personalized treatment strategies. However, its rapid adoption presents regulatory, ethical, and institutional challenges, particularly in higher education foundations that oversee health research. This paper examines policies governing AI-driven health research, focusing on regulatory frameworks, ethical guidelines, and institutional policies that shape AI applications in academia. At the global and national levels, regulations such as the EU AI Act and World Health Organization guidelines set standards for AI safety, transparency, and data protection in health research. Despite these frameworks, challenges persist, including data privacy concerns, algorithmic bias, and inconsistent ethical oversight. Ethical frameworks like the Ethical Regulatory Framework for AI emphasize accountability, fairness, and continuous monitoring to ensure responsible AI deployment. Higher education institutions play a crucial role in developing AI governance frameworks that balance innovation with compliance. However, inconsistencies in institutional policies create gaps in regulatory enforcement and ethical standards. Addressing these issues requires harmonized policies, interdisciplinary collaboration, and proactive stakeholder engagement. This paper highlights the role of AI in optimizing research methodologies, funding allocation, and regulatory compliance while discussing emerging challenges and future directions for AI-driven health research governance.

This work is licensed under a Creative Commons Attribution-Share Alike 4.0

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## Corresponding Author:

Yuris Tri Naili, Universitas Harapan Bangsa, Indonesia

Email: yurist@uhb.ac.id

## 1. INTRODUCTION

The rapid advancement of Artificial Intelligence (AI) in health research has transformed the way medical studies are conducted, enabling faster data analysis, predictive modeling, and personalized treatment strategies [1]. AI-driven innovations hold great potential for enhancing healthcare outcomes; however, their integration into health research also introduces complex regulatory, ethical, and institutional challenges. As higher education foundations play a significant role in health research, there is a growing need for well-defined policies governing AI applications in this field. These policies must balance innovation with ethical and regulatory compliance, ensuring that AI-driven research adheres to global standards.

<footnote n="1">¹Department of Law, Faculty of Social Science, Universitas Harapan Bangsa, Indonesia</footnote>

<footnote n="2">² Department of Business Law, Universitas Prasetiya Mulya, Indonesia</footnote>

<footnote n="3">³ Department of Civil Law, Vasyl Stefanyk Precarpathian National University Ivano-Frankivsk, Ukraine</footnote>
One of the key aspects of AI governance in health research is the regulatory framework that oversees its application. At the global level, institutions such as the World Health Organization (WHO) and the European Union (EU) have established legal and ethical guidelines for AI in healthcare [1]. The EU AI Act, for instance, sets standards for AI transparency, risk assessment, and patient safety, while the WHO emphasizes fairness, accountability, and privacy in AI-driven health applications [2]. Additionally, national governments have implemented localized regulations to address jurisdiction-specific concerns, ensuring that AI is developed and used responsibly in healthcare settings.

Beyond regulatory oversight, ethical governance remains a critical challenge in AI-driven health research. Issues such as data privacy, algorithmic bias, and transparency have raised concerns among researchers, policymakers, and the general public [3]. Ethical review processes are essential to ensuring that AI models are not only technically robust but also fair and inclusive. Various frameworks, including the Ethical Regulatory Framework for AI, have been developed to guide AI research governance by incorporating structured ethical reviews, penalties for non-compliance, and continuous improvement mechanisms [4].

Higher education institutions serve as major contributors to AI-driven health research, necessitating the development of institutional policies that align with global ethical and regulatory standards [5]. Universities and research centers are increasingly adopting AI governance frameworks that emphasize neutrality, transparency, legality, and innovation. However, the lack of standardized institutional policies across different research environments has led to inconsistencies in AI governance [6]. Establishing harmonized policies at the institutional level is crucial for fostering responsible AI research while ensuring compliance with both national and international regulations.

This paper explores the regulatory, ethical, and institutional policies governing AI-driven health research in higher education foundations. It examines the global and national regulatory frameworks, ethical challenges and recommendations, and institutional policies shaping the responsible use of AI in health research. Additionally, the role of AI in optimizing research methodologies, funding allocation, and regulatory compliance is discussed. As AI continues to evolve, higher education institutions must actively engage in policy development to ensure that AI-driven health research remains ethical, transparent, and beneficial to global healthcare systems.

# 2. REGULATORY FRAMEWORKS

## 2.1. Global and National Regulations

The EU AI Act and various national legislations serve as the foundational regulatory framework for Artificial Intelligence in healthcare, aiming to establish safety, innovation, and data protection as core principles [1] [2]. The EU AI Act, in particular, classifies AI applications into risk categories, ensuring that high-risk AI systems, such as those used in diagnostics, treatment recommendations, and robotic surgeries, meet stringent transparency, accountability, and fairness standards. These regulations mandate bias mitigation, data security, and human oversight, preventing potential risks associated with automated decision-making in clinical settings. Additionally, national governments across different regions have implemented complementary policies tailored to their local healthcare infrastructures, addressing specific legal, ethical, and technological concerns. For example, the United States enforces AI healthcare compliance through the FDA and HIPAA regulations, while Canada and Australia emphasize AI governance through privacy protection acts and ethical AI frameworks. These national-level approaches collectively aim to ensure responsible AI development and deployment while fostering innovation in medical research and healthcare delivery.

Beyond regional regulations, international organizations such as the WHO play a crucial role in shaping ethical AI governance on a global scale. The WHO has released guidelines to ensure ethical AI deployment in health, emphasizing fairness, accountability, transparency, and human rights protection [7] [8]. These guidelines advocate for AI systems that support rather than replace human decision-making, reinforcing physician oversight and patient-centered care. Additionally, the WHO highlights the importance of cross-border collaboration, recommending that countries work together to develop standardized AI policies that prevent regulatory fragmentation and inconsistencies. The WHO also stresses the need for independent audits, continuous monitoring, and adaptive governance mechanisms to address emerging challenges such as algorithmic bias, data privacy risks, and AI safety concerns.

## 3.2 Ethical Regulatory Frameworks

Frameworks like the Ethical Regulatory Framework for AI provide structured guidance for regulatory bodies in establishing ethical AI governance, ensuring that AI technologies in healthcare adhere to fairness, accountability, and transparency principles [3]. The ERF-AI outlines key governance mechanisms, including
ethical review processes, which require AI systems to undergo rigorous evaluations before deployment in medical research, diagnostics, and patient care. These evaluations assess potential biases, explainability, and risk mitigation strategies, ensuring that AI applications align with ethical and legal standards. Additionally, the framework introduces incentive-based mechanisms, encouraging AI developers and healthcare institutions to prioritize responsible AI practices. By rewarding organizations that implement fair AI models and robust compliance measures, regulatory bodies can foster an AI ecosystem that prioritizes patient safety and ethical integrity.

Beyond ethical reviews and incentives, the ERF-AI also incorporates penalty structures and continuous improvement mechanisms to reinforce compliance and adaptability in AI governance [4]. Penalties for non-compliance ensure that AI developers and healthcare providers adhere to ethical and legal obligations, deterring the use of unsafe, biased, or non-transparent AI systems. Additionally, the framework emphasizes continuous improvement, recognizing that AI technologies evolve rapidly and require regular updates to governance policies. By integrating feedback loops, independent audits, and AI impact assessments, regulatory bodies can proactively address emerging ethical challenges in AI-driven healthcare. This approach ensures that AI governance remains dynamic and responsive, allowing policies to adapt to technological advancements while maintaining a strong ethical foundation. As AI continues to reshape healthcare, frameworks like the ERF-AI serve as critical tools in establishing trustworthy, patient-centric AI applications that align with both regulatory and societal expectations.

# 3. ETHICAL GUIDELINES

## 3.1 Ethical Governance

Ethical governance systems, such as those developed in South Korea, have expanded traditional clinical trial frameworks to incorporate AI-specific oversight mechanisms, ensuring that AI-driven health research meets rigorous ethical and regulatory standards [4]. Unlike conventional clinical trials, which primarily focus on drug testing and medical interventions, AI research introduces unique challenges, such as algorithmic transparency, bias mitigation, and explainability. To address these issues, South Korea's governance system includes additional review stages, requiring AI-based health technologies to undergo ethical validation, fairness assessments, and independent audits before clinical deployment. These extended trial processes ensure that AI models are not only scientifically accurate but also ethically responsible, minimizing risks such as biased decision-making, patient data exploitation, or unintended clinical consequences.

In addition to national governance models, international ethical frameworks also play a vital role in regulating AI-driven health research. The Global Forum on Bioethics in Research (GFBR) emphasizes the need for accountability, informed consent, and fair partnerships in the development and deployment of AI health technologies [9]. Accountability mechanisms ensure that AI developers and researchers remain transparent about AI decision-making processes, addressing concerns related to explainability and trustworthiness. The forum also stresses the importance of informed consent, advocating for patient and clinician awareness regarding how AI models process medical data and influence clinical decisions. Furthermore, fair partnerships in AI research promote equitable collaboration between developed and developing nations, preventing AI-driven healthcare innovations from disproportionately benefiting certain populations while leaving others behind.

## 3.2 Challenges and Recommendations

Ethical challenges in AI-driven health research primarily revolve around data privacy, algorithmic bias, and transparency, raising concerns about patient rights, fairness, and accountability [10]. Data privacy is a critical issue, as AI models rely on vast amounts of sensitive medical data to improve predictions and decision-making. Without robust data governance policies, there is a risk of unauthorized access, data breaches, and potential misuse of patient information. Moreover, algorithmic bias presents a significant ethical dilemma, as AI models trained on non-representative datasets may produce discriminatory outcomes, disproportionately affecting certain demographics based on gender, ethnicity, or socioeconomic status. Additionally, lack of transparency in AI decision-making further complicates its ethical deployment, as many black-box AI models provide limited insight into how they reach specific clinical conclusions. This opacity can undermine trust between healthcare providers, patients, and regulatory bodies, making it difficult to ensure that AI recommendations align with medical best practices and patient well-being [11].

To mitigate these ethical concerns, experts recommend the implementation of robust ethical guidelines, continuous monitoring, and active stakeholder engagement in AI health research governance [12]. Comprehensive ethical guidelines should establish clear standards for data protection, fairness, and model interpretability, ensuring that AI systems adhere to human-centered principles [10] [11]. Continuous
monitoring is also essential, requiring the deployment of AI auditing mechanisms, bias detection tools, and real-time performance evaluations to identify and correct potential ethical risks. Furthermore, stakeholder engagement—including healthcare professionals, AI researchers, patients, and policymakers—is crucial for fostering collaborative decision-making and ensuring that AI technologies reflect diverse perspectives and societal values.

# 4. INSTITUTIONAL POLICIES

## 4.1 Higher Education Institutions (HEIs)

Higher Education Institutions (HEIs) are actively developing AI governance frameworks to strike a balance between technological innovation and regulatory compliance, ensuring that AI-driven health research aligns with ethical, legal, and societal expectations [5]. These frameworks emphasize key principles such as legality, neutrality, transparency, and innovation, fostering an environment where AI can advance medical research while maintaining ethical integrity. By integrating risk assessment protocols, bias mitigation strategies, and responsible data usage policies, HEIs aim to create trustworthy AI ecosystems that support equitable and inclusive AI applications in healthcare. Additionally, interdisciplinary collaborations between AI researchers, ethicists, and medical professionals within HEIs contribute to the refinement of AI policies, ensuring that regulatory standards evolve in tandem with rapid technological advancements.

Despite these efforts, there remains a pressing need for standardized policies across institutions to promote consistent ethical practices and regulatory alignment [13][14]. Currently, variations in institutional AI policies create disparities in research ethics, data governance, and AI validation criteria, leading to inconsistencies in compliance and risk management. Without a unified approach, institutions may struggle to implement uniform accountability mechanisms, potentially compromising AI transparency and patient safety. To address this challenge, collaborative policy development initiatives, driven by international regulatory bodies, academic consortia, and government agencies, are essential to establish harmonized AI governance frameworks. These efforts should focus on developing universal ethical standards, AI auditing mechanisms, and data-sharing agreements, enabling HEIs to operate under a cohesive regulatory structure while continuing to drive AI-driven healthcare innovation responsibly.

## 4.2 Research Governance

Formal governance frameworks in health research, such as those established in the United Kingdom (UK), play a crucial role in guiding ethical oversight and peer review processes to ensure the integrity and reliability of research outcomes [15]. These frameworks set clear standards for data protection, informed consent, and research transparency, creating a structured approach to evaluating AI-driven health studies. By enforcing ethical review protocols and regulatory compliance measures, these governance systems help mitigate potential risks related to data privacy, algorithmic bias, and patient safety. However, a notable challenge is the tendency to prioritize quantitative research methodologies, often overlooking the significance of qualitative insights, such as patient perspectives, social determinants of health, and contextual factors influencing AI adoption.

The emphasis on quantitative data in formal governance structures may limit the depth of ethical evaluations by focusing primarily on numerical performance metrics, such as AI accuracy, efficiency, and predictive capabilities, rather than the broader societal and ethical implications of AI in healthcare [15]. This approach can lead to gaps in understanding how AI-driven health technologies affect diverse populations, particularly in underrepresented or marginalized communities. To ensure a more holistic governance strategy, institutions should integrate qualitative research frameworks that incorporate ethical deliberation, human-centered AI assessments, and interdisciplinary perspectives. Strengthening governance models by balancing quantitative rigor with qualitative depth can enhance the ethical and regulatory foundations of AI-driven health research, promoting more inclusive and socially responsible AI applications.

# 5. ROLE OF AI IN HEALTH RESEARCH

## 5.1 Optimizing Research Methodologies

AI significantly enhances research methodologies by revolutionizing data analysis, predictive modeling, and personalized treatment planning, leading to more efficient and accurate health research outcomes [10]. Advanced machine learning algorithms can process vast amounts of medical data at unprecedented speeds, identifying patterns, correlations, and anomalies that might be overlooked in traditional research methods. AI-driven predictive modeling enables researchers to forecast disease progression, treatment efficacy, and patient outcomes, allowing for more targeted and proactive healthcare interventions. Additionally, AI-powered
personalized treatment plans leverage genomic data, medical histories, and real-time patient monitoring to recommend individualized therapies, reducing trial-and-error approaches in medicine [16].

Beyond its role in data-driven research, AI also improves efficiency in study design, resource allocation, and scientific reproducibility [17]. Traditional research methodologies often require extensive manual data processing, which can be time-consuming and prone to human error. AI automates data cleaning, feature selection, and hypothesis generation, streamlining large-scale studies while ensuring higher accuracy and reliability. Moreover, AI-powered natural language processing (NLP) tools facilitate the systematic review of medical literature, enabling researchers to extract valuable insights from thousands of scientific papers in a fraction of the time. Despite its transformative potential, AI-driven research faces challenges, including bias in training data, lack of interpretability, and ethical concerns regarding data privacy. Addressing these limitations through transparent AI models, interdisciplinary collaboration, and ethical governance frameworks is essential to fully harness AI's potential in advancing health research and evidence-based medicine.

## 5.2 Funding Allocation

AI plays a transformative role in optimizing healthcare financing by enhancing governance, revenue generation, and strategic resource allocation, ensuring that funding is utilized efficiently and equitably [17]. By leveraging machine learning algorithms and predictive analytics, AI can assess financial trends, forecast budgetary needs, and detect inefficiencies in healthcare spending. AI-driven financial models help policymakers and administrators prioritize funding for critical healthcare services, reducing waste and mismanagement. Additionally, AI enhances fraud detection and risk assessment by identifying anomalous billing patterns and fraudulent claims, preventing financial losses while maintaining transparency and accountability in healthcare funding.

Beyond governance and fraud prevention, AI facilitates strategic purchasing and cost-effective decision-making in healthcare [18]. AI-powered procurement systems can analyze market trends, supplier reliability, and price fluctuations, enabling healthcare institutions to negotiate better contracts and optimize inventory management. By integrating real-time financial data with patient care needs, AI helps hospitals and clinics allocate resources dynamically, ensuring that medical equipment, pharmaceuticals, and staffing levels align with demand patterns and patient outcomes. As AI continues to evolve, its role in healthcare financing will expand, providing data-driven insights that enhance efficiency, reduce costs, and improve overall financial sustainability in healthcare systems.

## 5.3 Compliance with Global Health Regulations

AI plays a critical role in ensuring compliance with global health regulations by strengthening data protection, ethical governance, and transparency in health research and clinical applications [8]. With the increasing reliance on AI-driven analytics and automation, safeguarding patient data privacy has become a top priority. AI systems help enforce data protection laws, such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), by incorporating secure encryption, anonymization techniques, and access control mechanisms. These measures ensure that sensitive health information remains confidential and protected from unauthorized access. Additionally, AI enhances regulatory compliance by automating risk assessment, bias detection, and ethical auditing, ensuring that AI models adhere to fairness, accountability, and explainability principles. Through real-time monitoring and automated reporting, AI assists regulatory bodies in identifying potential compliance breaches and mitigating risks before they escalate.

Beyond data security, international collaboration and harmonized regulatory standards are essential for effective AI governance in healthcare [12]. Disparities in regional AI policies can create regulatory gaps, hindering cross-border AI research and technology deployment. By fostering global partnerships, AI governance frameworks can establish uniform ethical and legal guidelines, enabling seamless data sharing, interoperability, and AI validation across different healthcare systems. Organizations such as the WHO and the International Medical Device Regulators Forum (IMDRF) advocate for harmonized AI policies, ensuring that AI applications meet universal safety and ethical standards. Strengthening international cooperation in AI governance will help address challenges related to algorithmic bias, regulatory inconsistencies, and ethical AI deployment, ultimately promoting trust, transparency, and equitable access to AI-driven healthcare solutions worldwide.

## CONCLUSION

AI-driven health research in higher education is governed by regulatory frameworks, ethical guidelines, and institutional policies, ensuring responsible development and deployment of AI technologies. These frameworks optimize research methodologies, funding allocation, and regulatory compliance, fostering
innovation while safeguarding patient rights and data privacy. However, challenges such as algorithmic bias, transparency issues, and ethical oversight gaps require adaptive governance mechanisms and interdisciplinary collaboration. Strengthening global and national policies, integrating AI auditing systems, and promoting equitable access to AI-driven healthcare are crucial steps toward ethical and effective AI adoption. To address emerging challenges and fully harness AI's potential, continuous policy updates, stakeholder engagement, and proactive regulatory strategies remain essential.

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