Artificial Intelligence in Clinical Trials: U.S. Landscape, Laws, and Ethical Considerations

ABSTRACT

Objective:

Authors examined the current applications and potential impact of artificial intelligence (AI) in clinical trials with emphasis on regulatory considerations and ethical implications.

Methods:

A review of recent literature and regulatory documents was conducted, analyzing case studies, technological advancements, and regulatory frameworks related to AI integration in clinical trials in the United States of America.

Results:

AI applications in clinical trials span various domains, including trial design, patient recruitment, data analysis, and outcome prediction. In clinical trials, AI has shown promise in automating processes and improving efficiency. The FDA has received approximately 300 submissions referencing AI use in drug development since 2016. AI tools have demonstrated potential in analyzing real-world data, optimizing site selection, and enhancing participant recruitment. However, challenges persist in ensuring participant safety, data reliability, and addressing ethical concerns such as bias and privacy.

Conclusions:

While AI offers significant potential to streamline clinical trials and reduce costs, mindful implementation is necessary to maintain safe and high -quality patient care, scientific integrity, ethical standards and mitigate risks. Regulatory bodies, including the FDA, are developing frameworks to evaluate AI research tools and their generated data. Future research should focus on refining AI applications to enhance trial efficiency while prioritizing patient safety and data privacy.

INTRODUCTION

The integration of Artificial Intelligence (AI) in clinical trials has emerged as a transformative force in the United States healthcare system. While AI offers significant benefits in clinical trials, including cost reduction and improved efficiency, it also presents complex challenges in governance, regulation, and ethical implementation. Authors aim to analyze specific AI applications in U.S. clinical trials, focusing on case studies, regulatory frameworks, and ethical considerations.

BACKGROUND

The evolution of AI in clinical trials can be traced back to early data analysis tools, but recent advancements in machine learning and natural language processing have dramatically expanded AI's capabilities. Today, AI is being utilized in various aspects of clinical trials, from patient recruitment to data analysis and drug discovery.

The relevance of AI in clinical trials has become increasingly apparent, driven by the need for improved efficiency, accuracy, and cost-effectiveness in drug development and patient care. The U.S. Food and Drug Administration (FDA) has been adapting regulations to manage AI's rapid development in healthcare, focusing on patient safety and the life cycle of AI tools (News-Medical.net, 2024). Ethical AI applications can be understood as a systematic framework to guide the responsible implementation of AI in trial design, patient recruitment, data analysis, and outcome prediction (Rosenstrauch, 2023). For U.S. clinical trials, this ethical framework is crucial in evaluating and potentially accepting or rejecting AI technologies based on their impacts on trial participants, healthcare systems, and broader societal implications. The emphasis on addressing risks and challenges associated with AI implementation directly applies to clinical trial settings, where issues of patient autonomy, data privacy, and safety are paramount. Authors highlight that AI ethics can be understood as a systematic normative reflection based on a holistic, multicultural framework of interdependent values, principles, and actions (Rosenstrauch, 2023). This framework may guide societies in responsibly dealing with both known and unknown effects of AI on humans, societies, and ecosystems and have the potential to transform healthcare (Rosenstrauch, 2022).

METHODS

This work employed a comprehensive approach, including literature review, case study analysis, and examination of U.S. laws, federal regulations and ethical guidelines pertaining to AI in clinical trials.

RESULTS

Case Studies

Clinical trial safety

The FDA highlights the transformative role of AI in clinical trial design and research. AI technologies are increasingly utilized to streamline processes by extracting and organizing data from electronic health records and medical claims, as well as other unstructured data sources. This integration aims to enhance the safety and reliability of clinical trials, making them more agile and inclusive. The FDA is committed to fostering innovation while ensuring patient safety through a flexible regulatory framework (FDA, 2024).

AI approach for Clinical Trial Cohort Optimization

The study presents an AI approach for Clinical Trial Cohort Optimization (AICO), utilizing transformer-based natural language processing to analyze eligibility criteria and evaluate them against real-world data. This method aimed to enhance the diversity and generalizability of clinical trial populations by broadening eligibility criteria. A case study focused on breast cancer trial design illustrates the effectiveness of this approach in improving the applicability of trial results to real-world patients (Liu, X., et al., 2021).

Drug Discovery

Insilico Medicine is leveraging AI technologies to design potential new drugs, with one candidate demonstrating promising initial results in mice. The company employs deep learning and big data analytics to facilitate in silico drug discovery across a range of medical conditions, including fibrosis, immunology, oncology, and central nervous system disorders. This innovative approach aims to accelerate the drug development process and improve treatment outcomes (Insilico Medicine, 2025).

Identification of Drug Candidates

Benevolent AI is employing artificial intelligence to analyze extensive scientific literature and data, aiming to identify potential drug candidates and predict their efficacy. This AI-driven methodology has significantly accelerated drug discovery timelines and enhanced the success rates of clinical trials, demonstrating the transformative impact of technology on pharmaceutical development (Benevolent AI, 2025).

Oncology Trials

AI algorithms are utilized in oncology trials to analyze medical images, including X-rays, MRIs, and CT scans. These algorithms assist in identifying tumors, assessing their size and location, and monitoring treatment responses. By enhancing the accuracy and speed of diagnosis and treatment planning, AI contributes to more effective patient management in cancer care (Kumar et al., 2023).

Diabetic Retinopathy Screening

A clinical trial conducted by the FDA in collaboration with AI developers focused on using AI for diabetic retinopathy screening. The AI system demonstrated a 91% accuracy in detecting diabetic retinopathy, potentially saving $3.2 billion in annual healthcare costs and improving early detection for millions of Americans (FDA, 2024).

Clinical Trial Recruitment

A study by Nature Digital Medicine revealed that AI-powered patient recruitment can reduce clinical trial costs by 70% and expedite timelines by up to 40% (Clinion, 2024). In a specific U.S. oncology trial, AI-driven recruitment increased enrollment rates by 45%, potentially saving $2.5 million in recruitment costs.

U.S. Laws and Regulations Governing AI in Clinical Trials

Regulatory considerations need to be given to clinical trials. Currently there are several active key laws governing clinical trials in the United States of America (USA). These laws work in conjunction with regulations to govern the conduct of clinical trials in the USA, including those involving AI technologies.

Federal Food, Drug, and Cosmetic Act

This is the primary law governing clinical trials for drugs, biologics, and medical devices in the United States (FDA, 2025). The Federal Food, Drug, and Cosmetic Act provides the FDA with authority to regulate medical devices, including AI and Machine Learning software used in clinical trials (FDA, 2023).

Public Health Service Act

This act provides additional authority for the regulation of biological products. It grants the federal government enhanced authority to regulate biological products, ensuring their safety, efficacy, and quality. This legislation establishes a framework for the oversight of various biological products, including vaccines, blood products, and gene therapies, thereby playing a critical role in public health protection (DHHS, 2025).

Title 21 of the Code of Federal Regulations

Title 21 of the Code of Federal Regulations (CFR) outlines essential regulations governing the protection of human subjects in research, the role of Institutional Review Boards (IRBs), and the processes for investigational new drug applications and marketing approvals (FDA, 2023).

Specific parts of Title 21 include:

21 CFR Part 50: Protection of Human Subjects

This section establishes the requirements for obtaining informed consent from research participants, ensuring that individuals are adequately informed about the nature of the research, potential risks, and their rights. It emphasizes the ethical treatment of human subjects and mandates that researchers prioritize participant safety and autonomy.

21 CFR Part 56: Institutional Review Boards

Part 56 outlines the requirements for IRBs, which are committees responsible for reviewing and approving research involving human subjects. This section details the composition, responsibilities, and operational procedures of IRBs, ensuring that they effectively protect the rights and welfare of participants in clinical trials.

21 CFR Part 312: Investigational New Drug Application

This part describes the process for submitting an Investigational New Drug (IND) application to the FDA. It includes requirements for the content and format of the application, the phases of clinical investigations, and the responsibilities of sponsors and investigators. The IND process is crucial for ensuring that new drugs are tested for safety and efficacy before they can be marketed.

21 CFR Part 314: Applications for FDA Approval to Market a New Drug

Part 314 outlines the procedures for submitting applications for FDA approval to market new drugs. It includes requirements for the New Drug Application (NDA), which must demonstrate that the drug is safe and effective for its intended use. This section also covers post-marketing requirements and the FDA's authority to withdraw approval if safety concerns arise.

CFR collectively ensure that the development and approval of new drugs are conducted ethically and safely, protecting human subjects throughout the research process (FDA, 2023).

Common Rule

Title 45 of the CFR, specifically Part 46, commonly referred to as The Common Rule, establishes the federal regulations for the protection of human subjects involved in clinical research. This regulation is crucial for ensuring ethical standards and safeguarding the rights and welfare of participants in research studies (HHS, 2022).

Key aspects include:

1. Informed Consent: Researchers must obtain voluntary and informed consent from participants, ensuring they understand the nature of the research, potential risks, and their rights.

2. Institutional Review Boards (IRBs): The regulation mandates the establishment of IRBs to review and approve research protocols, ensuring that the rights and welfare of participants are protected.

3. Risk Assessment: Researchers are required to assess and minimize risks to participants, ensuring that the potential benefits of the research outweigh any risks involved.

4. Vulnerable Populations: Additional protections are provided for vulnerable populations, such as children, prisoners, and individuals with cognitive impairments, to ensure their rights and welfare are prioritized.

5. Compliance and Oversight: Institutions conducting research must comply with these regulations and are subject to oversight to ensure adherence to ethical standards.

45 CFR Part 46 plays a vital role in promoting ethical research practices and protecting human subjects in clinical trials (HHS, 2022).

The Health Insurance Portability and Accountability Act

The Health Insurance Portability and Accountability Act (HIPAA) governs the privacy and security of health information. It establishes standards to protect sensitive patient data and ensures that individuals have rights over their health information.

Key components include:

1. Privacy Rule: This rule protects identifiable health data and grants individuals the legal right to access their health information.

2. Security Rule: A subset of the Privacy Rule, the Security Rule specifically focuses on safeguarding electronic health information that is created, received, maintained, or transmitted by covered entities.

3. Covered Entities: HIPAA applies to health care providers, health plans, and health care clearinghouses that handle protected health information (PHI).

4. Patient Rights: Under HIPAA, patients have the right to request copies of their health records and to receive notifications of any breaches of their health information.

HIPAA plays a crucial role in ensuring the confidentiality and security of health information in the healthcare system (HIPAA, 2021).

Pediatric Research Equity Act

The Pediatric Research Equity Act (PREA) mandates certain applications for new drugs and biologics to include data assessing their safety and effectiveness in pediatric populations. This act was established to address the lack of adequate research on medications used in children, ensuring that pediatric patients receive safe and effective treatments (FDA, 2024).

Key aspects include:

1. Mandatory Pediatric Studies: The PREA requires pharmaceutical companies to conduct pediatric studies for new drugs and biologics unless they qualify for a waiver or deferral. This ensures that the specific needs of children are considered during the drug development process.

2. Pediatric Study Plans: Sponsors must submit a pediatric study plan outlining how they will assess the drug's safety and efficacy in various pediatric age groups. This plan must be approved by the FDA before the drug can be marketed.

3. Inclusion of Pediatric Data: The act aims to ensure that drug labels contain relevant pediatric information, including dosing and safety data, to guide healthcare providers in prescribing medications to children.

4. Exemptions: Certain products, such as those intended for rare diseases affecting fewer than 200,000 people, may be exempt from these requirements.

PREA plays a vital role in enhancing the understanding of how new drugs and biologics affect pediatric populations, ultimately leading to better healthcare outcomes for children (FDA, 2024).

Best Pharmaceuticals for Children Act

The Best Pharmaceuticals for Children Act (BPCA) is a significant legislative measure that aims to improve the safety and efficacy of medications used in pediatric populations. It provides various incentives for pharmaceutical companies to conduct studies specifically focused on children (BPCA, 2025).

Key features include:

1. Incentives for Pediatric Studies: The BPCA offers an additional six months of marketing exclusivity for drug manufacturers that voluntarily conduct pediatric studies as requested by the FDA. This incentive encourages companies to invest in research that may not be financially viable without such support.

2. Focus on Off-Patent Drugs: The act allows the National Institutes of Health (NIH) to fund studies on off-patent drugs, addressing gaps in pediatric research where manufacturers may be unwilling to invest.

3. Collaboration with FDA: The BPCA works in conjunction with the FDA to issue Written Requests (WRs) to manufacturers, outlining the necessary pediatric studies. If a manufacturer declines to conduct the studies, the NIH can step in to sponsor the research.

4. Impact on Pediatric Labeling: Since its enactment, the BPCA has led to significant increases in the number of drugs studied in children, resulting in updated labeling that provides critical information on the safe and effective use of medications in pediatric populations.

BPCA promotes the development of safe and effective treatments for children, addressing the historical lack of pediatric-specific research in the pharmaceutical industry (BPCA, 2025).

The Orphan Drug Act

The Orphan Drug Act (ODA) enacted in 1983 encourages the development of drugs for rare diseases, which are conditions affecting fewer than 200,000 individuals in the United States. ODA provides several incentives aimed at pharmaceutical companies and researchers to stimulate the development of orphan drugs (FDA, 2018).

Key features include:

1. Market Exclusivity: The ODA grants a seven-year period of market exclusivity for orphan drugs once they receive FDA approval. This exclusivity prevents the FDA from approving similar drugs for the same indication during this time, allowing companies to recoup their research and development costs more effectively.

2. Tax Incentives: The act offers a tax credit of up to 50% for clinical testing expenses incurred during the development of orphan drugs. This financial incentive helps alleviate some of the costs associated with bringing a new drug to market.

3. Grants and Assistance: The ODA provides grants to support research and development efforts for orphan drugs. Additionally, the FDA offers assistance in study design and protocol development to help sponsors navigate the regulatory process.

4. Waivers for FDA Fees: Companies developing orphan drugs may be eligible for waivers of certain FDA application fees, further reducing the financial burden associated with drug development.

ODA has significantly increased the number of orphan drugs developed and approved, addressing the unmet medical needs of patients with rare diseases (FDA, 2018).

Food and Drug Administration Safety and Innovation Act

The Food and Drug Administration Safety and Innovation Act (FDASIA), signed into law on July 9, 2012, expanded the FDA's authorities and enhanced its ability to protect and promote public health. FDASIA introduced several key provisions aimed at improving the drug and medical device approval processes, fostering innovation, and ensuring the safety of the drug supply chain (FDA, 2018).

Key features include:

1. User Fees: FDASIA authorized the collection of user fees from the pharmaceutical and medical device industries to fund the FDA's review processes for new drugs, medical devices, generic drugs, and biosimilars. This funding helps maintain a skilled workforce to evaluate the safety and efficacy of new products.

2. Breakthrough Therapy Designation: The act introduced a new designation for drugs that show promise in treating serious or life-threatening conditions, allowing for expedited development and review processes. This aims to provide patients with faster access to potentially life-saving therapies.

3. Stakeholder Engagement: FDASIA emphasized the importance of involving patients and other stakeholders in the drug development process. The FDA initiated programs to gather input from patients about their experiences and needs related to specific diseases.

4. Drug Supply Chain Safety: The act included provisions to enhance the safety of the drug supply chain, addressing challenges posed by the increasing globalization of drug manufacturing. This includes new authorities for the FDA to inspect foreign facilities and enforce compliance with safety standards.

FDASIA modernized the FDA's regulatory framework, promoting innovation, and ensuring that the agency can effectively safeguard public health (FDA, 2018).

The Food and Drug Administration Amendments Act

The Food and Drug Administration Amendments Act of 2007 (FDAAA) significantly expanded the requirements for the registration of clinical trials on ClinicalTrials.gov, a publicly accessible database managed by the National Library of Medicine. FDAAA aimed to enhance transparency and accountability in clinical research (FDA, 2007).

Key aspects include:

1. Expanded Registration Requirements: The FDAAA mandated that sponsors and responsible parties register a broader range of clinical trials, specifically those involving FDA-regulated drugs, biologics, and devices. This includes trials that were previously exempt from registration requirements.

2. Applicable Clinical Trials (ACTs): The act defined "applicable clinical trials" and established criteria for which trials must be registered. This includes interventional studies that are not solely focused on device feasibility and those that are not in Phase 1 of testing.

3. Results Reporting: In addition to registration, the FDAAA requires that summary results information be reported for applicable clinical trials within specified time frames, independent of journal publication decisions. This aims to ensure that the outcomes of clinical trials are publicly available.

4. Compliance and Accountability: The FDAAA introduced potential penalties for noncompliance, including civil monetary penalties and withholding of grant funding from HHS agencies. This was intended to encourage adherence to the registration and reporting requirements.

The FDAAA improved the availability of information about clinical trials, thereby enhancing public trust in the clinical research process and ensuring that patients and healthcare providers have access to important trial data (FDA, 2007).

21st Century Cures Act

The 21st Century Cures Act (CCA), signed into law on December 13, 2016, is a landmark piece of legislation aimed at accelerating medical product development and enhancing patient access to innovative treatments. A significant aspect of CCA is its encouragement of the use of real-world evidence (RWE) and novel technologies in clinical trials, which indirectly supports the integration of AI in the healthcare sector.

Key points include:

1. Real-World Evidence: CCA promotes the incorporation of RWE, which includes data collected from various sources outside of traditional clinical trials, such as electronic health records and patient registries. This approach allows for a more comprehensive understanding of treatment effects in diverse populations.

2. Modernized Clinical Trials: By facilitating the use of RWE, the CCA aims to modernize clinical trial designs, making them more efficient and reflective of real-world patient experiences. This can lead to faster approvals for new therapies and devices.

3. Support for Innovative Technologies: CCA encourages the adoption of novel technologies, including AI and machine learning, to enhance data analysis and improve decision-making processes in clinical research. This integration can lead to more personalized and effective treatment options.

4. Patient-Centric Approach: CCA emphasizes the importance of incorporating patient perspectives into the drug development process, ensuring that treatments meet the actual needs of patients.

CCA represents a significant shift towards a more adaptive and responsive healthcare system, leveraging real-world data and innovative technologies to improve patient outcomes and streamline the development of new medical products (U.S. Congress, 2016).

FDA's AI/ML-Based SaMD Action Plan

The FDA's AI/ML-Based Software as a Medical Device (SaMD) Action Plan outlines the agency's comprehensive approach to regulating medical devices that incorporate AIand machine learning (ML) technologies. Released in January 2021, the action plan addresses the unique challenges posed by these rapidly evolving technologies, particularly in the context of clinical trials and patient safety (FDA, 2021).

Key components include:

1. Regulatory Framework Development: The FDA aims to establish a regulatory framework that accommodates the adaptive nature of AI/ML technologies. This includes guidelines for premarket review processes and modifications to existing devices based on real-world performance data.

2. Good Machine Learning Practices: The action plan emphasizes the importance of developing good machine learning practices to ensure the reliability and safety of AI/ML algorithms used in medical devices. This includes recommendations for data management, algorithm validation, and performance monitoring.

3. Patient-Centered Approach: The FDA is committed to fostering a patient-centered approach by enhancing transparency regarding how AI/ML devices function and the data they use. This aims to build trust among healthcare providers and patients.

4. Real-World Performance Monitoring: The action plan encourages the advancement of real-world performance monitoring pilots to continuously assess the effectiveness and safety of AI/ML-enabled devices post-market.

5. Stakeholder Engagement: The FDA seeks ongoing feedback from stakeholders, including industry representatives, healthcare professionals, and patients, to refine its regulatory strategies and ensure they meet the needs of all parties involved.

The FDA's AI/ML-Based SaMD Action Plan represents a proactive effort to ensure that AI and ML technologies in medical devices are developed and used safely and effectively, ultimately improving patient care and outcomes (FDA, 2021).

Global Community and Ethical AI

Ethical considerations for clinical trials need to be given. Researchers identified unique challenges in assessing social value, ensuring scientific validity, and navigating complex consent processes in AI clinical trials for diabetic retinopathy screening (Emanuel et al., 2024). Ensuring fair participant selection and addressing potential bias in AI algorithms used for recruitment are critical ethical concerns (Emanuel et al., 2024).

While progress has been made on ethical AI (e-AI) integration, a universal global standard on e-AI is still outstanding (Rosenstrauch et al., 2023). A global convergence is emerging around five ethical principles: transparency, justice and fairness, non-maleficence, responsibility, and privacy. Challenges remain in addressing potential risks such as autonomy loss, bias, privacy violations, and unintended consequences (Gupta, 2023). The authors stress the importance of developing comprehensive ethical guidelines to ensure AI benefits humanity while mitigating potential harms.

Risk Mitigation Strategies for AI in Clinical Trials

Considerations for implementing ethical AI solutions (e-AIS) in healthcare management Rosenstrauch (2023) are equally applicable to clinical trials by

· Identifying appropriate use cases for AI

· Assembling diverse and inclusive teams for AI development

· Building transparency and explainability into AI systems

· Implementing robust data governance policies

· Protecting patient privacy and data security

· Establishing clear guidelines and standard operating procedures

· Developing mechanisms for stakeholder feedback and human oversight

· Conducting independent audits of AI systems

· Engaging with regulatory bodies to stay updated on best practices and regulations

These strategies leverage e-AIS to improve cost-effectiveness and patient outcomes in clinical trials while minimizing ethical risks and ensuring alignment with regulations and organizational values.

DISCUSSION

Interpretation

The case studies demonstrate AI's potential to significantly reduce costs and improve efficiency in clinical trials. However, they also highlight the need for careful consideration of ethical implications and regulatory compliance.

Implications

“Clinical Trial Stakeholders” must invest in robust AI governance frameworks for clinical trials. There is a need for standardized approaches to AI implementations in clinical trials, balancing innovation with patient safety and privacy concerns. Continuous education for healthcare professionals and other clinical trial stakeholders on AI technologies and their ethical implications is crucial.

Limitations

The rapidly evolving nature of AI technology, laws and regulations means that some findings may become outdated quickly. The focus on U.S. regulations limits the global applicability of some findings, especially with the existing and emerging data privacy regulations in the European Union. Long-term impacts of AI on clinical trials are still uncertain and will require ongoing research.

CONCLUSION

AI in clinical trials offers significant potential for improving efficiency and reducing costs in the U.S. healthcare system. However, its implementation must be carefully governed to ensure patient safety, data privacy, and ethical use. As AI continues to advance, ongoing collaboration between healthcare organizations, policymakers, and technology developers will be crucial to harness its full potential while maintaining ethical standards and regulatory compliance.

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Retrieved from https://www.benevolent.com

Clinion. (2024). AI in Clinical Trials: Key to Accelerated Timelines & Reduced Costs.

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DISCLAIMER

This article provides an overview of AI applications in U.S. clinical trials and related regulatory frameworks. The information presented is for general informational purposes only and should not be considered as legal advice, medical guidance, or a substitute for professional consultation.

The field of AI in healthcare and clinical research is rapidly evolving, and regulations and best practices may change. Readers are advised to consult with qualified professionals and refer to the most current official sources, including the U.S. Food and Drug Administration (FDA), other relevant regulatory bodies, for up-to-date information and guidance including policy changes by the U.S. government at https://www.congress.gov/browse/policyarea.

The authors, publishers, and affiliated institutions do not warrant the completeness, accuracy, or currency of the information provided. They shall not be liable for any loss, damage, or injury directly or indirectly caused by or resulting from the use of or reliance on any information contained in this article. For specific questions about AI applications in clinical trials, regulatory compliance, or ethical considerations, please consult with appropriate legal, medical, or regulatory professionals.

Doreen Rosenstrauch [1,2,3], Neethi Gangidi [4], Muskaan Shahzad [5], Taikwa Costansia Masau [6], Arnav Gupta [7], Duke Otto Rosenstrauch Ortiz [8], Atul Gupta [9]

1 Founder and CEO, DrDoRo®Institute, Global Healthcare Consulting Firm est. 2008

Linkedin.com/company/DrDoRo

2 Professor, Global Health Services and Administration, The Business School,

University of Maryland, UMGC

3 Professor, Cybersecurity & AI, Bachelor of Applied Technology in AI & Robotics Program, HCC

4 Artificial Intelligence & Data Analyst Engineer, and former HCC student

Linkedin.com/Neethi Gangidi

5 Student, Bachelor of Applied Technology in Artificial Intelligence & Robotics Program, HCC

Linkedin.com/MuskaanShahzad

6 International Coordinator, DrDoRo®Institute

Linkedin.com/CostansiaMasau

7 Student, Bachelor of Software Engineering, University of Waterloo