The FDA Innovation through Data and AI Act (FDAi Act)

An Act to Revolutionize Healthcare Through the Deployment of the FDAi System, Ensuring Personalized Health Optimization, Accelerated Clinical Discovery, and Global Health Equity

Acknowledging the transformative potential of artificial intelligence in revolutionizing healthcare delivery and research, and recognizing the need to overcome the limitations of the current healthcare and regulatory framework, this Act seeks to establish the FDAi system. It aims to foster a healthcare ecosystem that is personalized, efficient, and equitable, transcending traditional barriers to healthcare innovation and delivery.

This bill highlights the urgency and necessity of leveraging AI and data analysis to improve health outcomes and decision-making. It emphasizes the FDA’s role in facilitating access to experimental therapies and minimizing barriers for those in urgent need, with a strong focus on transparency, rapid data collection, and personalized health insights.

This Act envisions a future where the collective health intelligence of the population is harnessed through cutting-edge technology, significantly advancing the FDA’s ability to fulfill its mandate of ensuring the safety and efficacy of foods, drugs, supplements, and additives.

The FDAi Act is intended to provide a personal FDAi agent to every citizen:

Personal FDAi Agent Integration: Deploy AI-powered agents that aggregate and analyze individuals’ health and behavioral data. These agents will be capable of understanding complex health conditions and lifestyle patterns to recommend optimal diets and treatments and monitor for potential side effects continuously.

Advanced Dietary and Treatment Optimization: Leverage AI to quantify the positive and negative effects of foods, drugs, and additives on an individual’s health, enabling tailored dietary and treatment plans that dynamically adjust to health changes and personal preferences.

Clinical Trial Connectivity: Simplify the process for individuals to discover and enroll in clinical trials relevant to their health conditions. The FDAi agent will facilitate seamless participation, including eligibility checks, consent processes, and integration with trial data collection, enhancing the speed and efficiency of clinical research.

Comprehensive Health Monitoring: Use wearables and other health-monitoring devices to collect real-time data. This capability ensures that the FDAi agent can proactively suggest adjustments to treatment plans based on real-world evidence, promoting early intervention and preventing adverse health outcomes.

Automated Research and Data Analysis: Aggregate and analyze data from a broad spectrum of sources, including clinical trials, electronic health records (EHRs), and real-world evidence, to publish up-to-date research findings. This approach will democratize access to the latest healthcare information, empowering individuals with knowledge previously confined to medical professionals.

Test and Procedure Scheduling: Integrate scheduling functionalities to ensure that necessary tests and medical procedures are timely and efficiently managed, reducing the administrative burden on individuals and healthcare providers.

How the personal FDAi agent and the broader initiative can transform the research landscape:

Decentralized, Continuous Research: Establish a system where FDAi agents conduct long-term, continuous research across the entire population. This approach enables the collection of vast datasets on health outcomes, treatments, diets, and lifestyle choices, providing an unparalleled resource for understanding human health and disease.
Removing Profit Motives from Research: By operating without the profit motive that underpins pharmaceutical-driven research, FDAi focuses on discovering and validating the most effective treatments, regardless of their patent status or commercial potential. This shift prioritizes patient outcomes over financial gain, addressing the current system’s bias towards patented molecules.
Reducing Research Costs: Utilize AI and decentralized data collection to reduce the costs associated with traditional clinical trials dramatically. By leveraging real-world data from the population and employing advanced data analytics, FDAi can identify treatment effects and potential new therapies more efficiently and at a fraction of the cost of conventional methods.
Open and Collaborative Research Model: Encourage an open-source ethos in medical research, where data and findings are shared freely among scientists, clinicians, and the public. This model fosters collaboration, accelerates the dissemination of knowledge, and ensures that innovations benefit the widest possible audience.
Ethical Data Utilization: Implement strict ethical guidelines and consent frameworks to ensure that individuals’ data is used responsibly and with respect for privacy. By building trust in the system, FDAi can ensure high levels of participation and data quality, crucial for the success of long-term research initiatives.
Personalized and Adaptive Clinical Trials: Develop personalized clinical trial methodologies that adapt to real-time individual responses. This approach accelerates the discovery of effective treatments and minimizes patient suffering by quickly identifying the most beneficial interventions for each individual.
Global Health Equity: Aim to reduce global health disparities by making the fruits of research accessible to all, regardless of geographic location or economic status. By focusing on effective and affordable treatments, FDAi can contribute to a more equitable health system worldwide.
Regulatory Innovation: Work with regulatory bodies to adapt current frameworks to AI-driven, decentralized research realities. This includes developing new standards for evaluating treatments based on real-world evidence and ensuring that regulatory processes facilitate rapid innovation while maintaining patient safety.

Preamble

WHEREAS the Food and Drug Administration (FDA) is mandated to ensure the safety and efficacy of food, drugs, supplements, and additives consumed by the American public;

WHEREAS the current methodologies for assessing the long-term impacts of these substances are limited and do not fully leverage the potential of modern data analysis and artificial intelligence technologies;

WHEREAS the rapid and inclusive collection of longitudinal health data from a diverse population is crucial for understanding the personalized effects of foods, drugs, supplements, and additives on health outcomes;

WHEREAS the development of an open, interoperable platform for health data sharing, combined with advanced AI-driven analysis, can revolutionize the understanding of health impacts and inform more effective and personalized healthcare decisions;

WHEREAS the safety, efficacy, and personalized understanding of the impacts of foods, drugs, supplements, and additives are critical to improving health outcomes;

WHEREAS the rapid collection and analysis of health data through advanced technologies can accelerate the discovery of effective treatments, particularly for the sick and dying;

WHEREAS the use of artificial intelligence (AI) can significantly enhance the ability of individuals and physicians to make informed health decisions;

WHEREAS the Food and Drug Administration (FDA) has a responsibility to facilitate access to experimental therapies, minimizing bureaucratic and financial barriers for those in urgent need of relief;

WHEREAS the Food and Drug Administration (FDA) is mandated to ensure the safety and efficacy of food, drugs, supplements, and additives consumed by the American public;

NOW, THEREFORE, be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled, that:

Section 1. Objective

This Act mandates the Food and Drug Administration (FDA) to:

Support the development and deployment of an AI agent to assist individuals and physicians in understanding the personal effects of foods, drugs, and supplements, and in making informed prescribing decisions.
Ensure that the FDA does not obstruct the efforts of the sick or dying to find relief due to a lack of data.
Make participation in clinical trials of experimental therapies effortless and cost-effective for the sick and dying, thereby accelerating the collection of safety and efficacy data.

Section 2. FDAi Platform

The FDA shall fund and oversee the development of the FDAi Platform, an open-source repository and suite of tools designed to:

Allow for the secure and voluntary sharing of time series data on symptom severity, medication and supplement intake, and dietary intake by individuals.
Employ causal inference algorithms to generate mega-studies that elucidate the frequency and magnitude of symptoms and health outcomes following exposure to various foods, drugs, supplements, and additives.
Facilitate the development of predictive models for the personalized effects of these substances on individual health outcomes.

Core Components

The FDAi Platform will comprise:

Data Silo API Gateway Nodes: To enable data exports from health apps and silos into PersonalFDA Nodes.
PersonalFDA Nodes: Local applications where individuals can securely store their health data and receive personalized insights from their Personal AI Agent.
Clinipedia: An open-access knowledge repository aggregating global data on the health effects of foods, drugs, supplements, and medical interventions.

OAuth2 API, Developer Portal, and SDKs

The FDAi Platform shall provide:

An OAuth2 API and a developer portal to enable any applications and wearable devices to register as data providers.
SDKs to facilitate the integration of these applications and devices with the FDAi Platform, including mechanisms for users to easily share their health data.

Section 3. FDAi AI Agent

Overview

The FDAi Platform shall include the development of a Personal AI Agent to:

Assist individuals in understanding the personal effects of the foods, drugs, and supplements they consume.
Aid physicians in making informed prescribing decisions by providing access to personalized health insights and global mega-study findings.
Clearly communicate the frequency and severity of all side effects associated with substances, focusing particularly on experimental therapies.
Automate participation in clinical trials of experimental therapies, minimizing the financial burden on participants and researchers. monitor patient health in real-time
optimize individual health outcomes through advanced data analysis and AI-driven insights
Help individuals understand the effects of the foods and drugs they consume.

Target Audience

Individuals seeking personalized health and wellness guidance.
Patients with chronic conditions requiring ongoing management.
Healthcare providers looking for data-driven patient management tools.

Key Features

Personal Health Analysis and Recommendations

AI-driven analysis of health data from EHRs, wearables, dietary inputs, and genetic information.
Personalized recommendations for diet, exercise, and lifestyle adjustments.
Prediction of potential health issues based on data trends and historical patterns.

Dietary Analysis and Optimization

Analysis of dietary habits to identify potential triggers for health issues.
Recommendations for dietary adjustments to mitigate symptoms of chronic conditions.
Integration with dietary tracking apps and services for seamless data collection.

Real-time Health Monitoring

Continuous monitoring of health data via wearable devices.
Alerts and recommendations in response to detected anomalies or potential health risks.
Integration with health monitoring devices and apps.

Clinical Trial Connectivity

Matching patients with relevant clinical trials based on their health profile and preferences.
Simplified enrollment and consent process within the FDAi agent interface.
Direct delivery of trial medications and instructions to participants’ homes.

Automated Research and Data Analysis

Aggregation and analysis of a wide range of health data for continuous research.
Identification of public health trends and insights.
Contribution to a global health data repository for research and analysis.

6. Browser-Extension-Based Autonomous Agent for Data Collection

Functionality: A browser extension that acts as an autonomous agent, capable of logging into user-authorized accounts to automatically download relevant health data, including pharmacy records, supplement receipts, grocery purchases, and more.
Data Sources Integration: Compatibility with major pharmacies (e.g., CVS, Walgreens), online grocery platforms (e.g., Instacart, Amazon Fresh), and supplement retailers.
Security and Privacy: Ensures user data is collected and transmitted securely, adhering to privacy regulations such as HIPAA and GDPR. Utilizes end-to-end encryption and requires explicit user consent for data access and collection.
User Control and Transparency: Users have full control over which accounts the extension can access and what data it collects, with the ability to revoke access at any time. A clear, transparent log is provided to users, detailing what data has been collected and when.
Seamless Integration with FDAi System: Collected data is seamlessly integrated
Browser Extension Development: The extension will be developed for major browsers (Chrome, Firefox, Safari) to ensure broad accessibility.
Autonomous Navigation and Data Extraction: Implementation of advanced algorithms to navigate websites, log in securely, download relevant documents, and extract key health data.
Compatibility and Interoperability: Ensures compatibility with various online platforms for pharmacy data, grocery purchases, and supplement receipts, requiring ongoing updates to maintain access as platforms evolve.

Future Features

Emotional and Cognitive Health Monitoring: Use voice and facial recognition technology to assess emotional well-being and cognitive function, offering timely interventions for mental health support.
Genetic and Microbiome Analysis: Integration with genetic testing and microbiome analysis services to provide deeper health insights and personalized recommendations.
Virtual Health Assistant Avatar: A virtual avatar interface for more interactive and engaging user experiences, providing health advice, reminders, and companionship.
Blockchain-based Health Data Security: Utilize blockchain technology for secure and tamper-proof storage of health data, ensuring privacy and data integrity.
Augmented Reality (AR) Health Visualization: AR visualizations of health metrics and anatomical information for educational and diagnostic purposes.
Telehealth Integration: Seamless integration with telehealth services for virtual consultations and health assessments.

Evaluation Metrics

To ensure the success and effectiveness of the FDAi Agent, we will use the following metrics for evaluation:

Data Collection Metrics:

Efficiency of Data Collection: This will be evaluated based on the speed and automation level of data collection processes.
Coverage of Data Collection: This will be assessed based on the breadth (variety of data types) and depth (level of detail) of data collected.

User Experience Metrics:

Ease of Setup: User satisfaction with the initial setup process of the FDAi Agent.
Control Over Data Collection: User satisfaction with their ability to control what data is collected and when.
Overall Usability: General user satisfaction with the usability of the FDAi Agent, including its interface and features.

Health Recommendation Metrics:

Accuracy of Health Recommendations: This will be measured by comparing the FDAi Agent’s recommendations with actual optimal health decisions.
Personalization of Health Recommendations: This will be evaluated based on user feedback about the relevance and specificity of the health recommendations to their individual needs and conditions.

Engagement and Outcome Metrics:

User Engagement Rates: This will be tracked by measuring active usage of the FDAi Agent, such as frequency of use and duration of sessions.
User Satisfaction Rates: This will be assessed through user surveys and feedback.
Clinical Trial Participation Rates: This will be measured by the number of users who participate in clinical trials facilitated by the FDAi Agent.
Improvement in Health Outcomes: This will be evaluated based on user feedback and analysis of health data, looking for positive changes in health conditions and lifestyle habits.

Section 4. Facilitating Access to Experimental Therapies

a) Clinical Trial Participation: The FDA shall streamline the process for the sick and dying to participate in clinical trials of experimental therapies, with the aim of minimizing the financial burden on participants.

b) Rapid Data Collection: The FDA shall prioritize the rapid collection and analysis of data from clinical trials to quickly determine the safety and efficacy of experimental treatments.

c) Transparent Reporting: The FDA shall ensure that the results from clinical trials, including the efficacy and side effects of experimental treatments, are publicly reported in an understandable and accessible manner.

The FDAi Act establishes a Bill of Patients’ Rights, asserting that:

The FDA shall not obstruct the efforts of the sick or dying to seek relief due to a lack of data.
The FDA must facilitate effortless participation in clinical trials for experimental therapies, striving to minimize the cost burden on participants.
Data collected from such clinical trials must be rapidly analyzed to determine the safety and efficacy of treatments, thereby informing both the participants and the wider public.

Section 5. Open Source and Collective Intelligence

The FDAi Platform shall be developed as an open-source project to ensure transparency, foster innovation, and leverage collective intelligence in the pursuit of understanding health impacts and improving health outcomes.

Open-Source Development

Leveraging open-source bounties to develop components of the FDA Innovation through Data and AI Act (FDAi Act) could potentially reduce costs, foster innovation, and accelerate development. Open-source bounties involve offering financial rewards for external developers to contribute to specific tasks or projects. This approach can tap into the global talent pool, bringing diverse perspectives and skills to the project.

Components Suitable for Open-Source Bounties

AI Development and Testing

Developing AI algorithms and models for analyzing health data could be highly suited for open-source contributions. Specific tasks, such as algorithm optimization, model training, and validation on public datasets, can be delineated and offered as bounties.

Infrastructure and Technology

Building the data storage, processing infrastructure, and cybersecurity measures could benefit from open-source collaboration, especially for components that do not require proprietary solutions.

Integration with Existing Systems

Creating APIs and integration tools for electronic health records (EHRs), wearable devices, and existing health databases could be accelerated through open-source contributions.

Open-Source Development Platform

The development of the platform itself, including tools for collaboration, version control, and a monorepo for the project, is inherently suitable for an open-source approach.

User Support and Training Material

Documentation, training materials, and support resources can be developed collaboratively, utilizing the knowledge and expertise of the open-source community.

Cost Savings and Considerations

Reduced Personnel Costs: By using open-source bounties, the project can significantly reduce the need for a large full-time staff, particularly for development and initial testing phases. This could potentially save tens to hundreds of millions of dollars, depending on the scale and complexity of the tasks.
Efficiency and Innovation: Open-source contributions can bring in fresh ideas and innovative solutions from a wide array of talents globally. This diversity can lead to more efficient problem-solving and creative approaches.
Quality and Maintenance: While open-source development can enhance innovation and reduce costs, it requires careful management to ensure the quality and security of contributions. Establishing a robust framework for reviewing, accepting, and integrating open-source contributions is essential. There might be ongoing costs associated with managing the open-source community, organizing bounty programs, and ensuring long-term maintenance and updates of the software.
Community Engagement: Offering bounties can stimulate community engagement and investment in the project. However, building and maintaining an active open-source community also requires effort and resources, including community managers, documentation, and support.
Cost Savings Compared to Traditional Closed Source Approach – Shifting significant portions of development to open-source bounties could reduce direct costs by 20% to 50% in the development phase. However, the exact savings would depend on the complexity of tasks outsourced, the effectiveness of community engagement, and the management of the open-source process. Costs related to community management, bounty administration, and quality control should be factored into the budget to ensure the success of the open-source development model.

Section 6. Collaborating Projects

The FDAi platform, as envisioned, would be designed to complement and enhance existing government health initiatives by providing a robust framework for data analysis, sharing, and privacy preservation. Tthe FDAi could be integrated with and support key programs such as the FDA’s Global Substance Registry System, theSentinel Initiative, the All of Us Research Program, and other relevant initiatives.

Integration with the Global Substance Registry System (GSRS)

The FDAi agent, as part of its core functionalities, is designed to synthesize and analyze vast amounts of data from diverse sources to provide actionable healthcare insights. Integrating the FDAi agent with the FDA’s GSRS would significantly enhance the agent’s ability to access and utilize detailed information on substances regulated by the FDA. GSRS, being a comprehensive database of substances, including drugs and their ingredients, could serve as a vital data source for the FDAi agent, enabling it to:

Access Up-to-date Information: Ensure that the FDAi agent has access to the most current and comprehensive data on substances, including their chemical properties, uses, regulatory status, and safety information. This is crucial for the agent to provide accurate and personalized healthcare recommendations.
Improve Personalized Healthcare Recommendations: Utilize detailed substance information from the GSRS to refine the FDAi agent’s algorithms for personalized healthcare advice. For example, the agent could identify potential drug interactions, allergies, or contraindications based on a user’s specific health profile and medication history.
Enhance Safety Monitoring: Leverage GSRS data to enhance the FDAi agent’s capabilities in monitoring and identifying potential safety issues related to substances. By analyzing real-world data and GSRS information, the agent could detect emerging safety signals much earlier, improving patient safety.
Support Regulatory Decisions: Assist the FDA in making informed regulatory decisions by providing insights derived from the integration of GSRS data with other health data analyzed by the FDAi agent. This could lead to more efficient and evidence-based regulatory processes.
Accelerate Research and Development: Enable more efficient identification of research gaps and opportunities by analyzing GSRS data alongside clinical and real-world evidence. This could help prioritize research efforts and support the development of new therapeutic options.

The integration would require robust data governance and privacy measures to ensure secure and ethical use of data. Moreover, it would necessitate technical infrastructure capable of handling the complexities of merging GSRS data with the FDAi’s decentralized AI-powered system, ensuring data integrity and reliability.

Overall, integrating the FDAi agent with the GSRS would significantly amplify the agent’s capabilities in providing personalized healthcare recommendations, enhancing drug safety monitoring, supporting regulatory decisions, and accelerating research and development, thereby fulfilling the FDAi’s mission to revolutionize healthcare through the power of AI and decentralized data.

Integration with the FDA’s Sentinel Initiative

Sentinel Initiative Background: The Sentinel Initiative is an FDA program aimed at monitoring the safety of FDA-regulated drugs, biologics, medical devices, and dietary supplements after they have reached the market. It uses a distributed data system to access electronic healthcare data from multiple sources while maintaining the privacy and security of this information.

FDAi Integration and Support:

Enhanced Data Analysis: The FDAi could provide advanced AI and machine learning tools to further analyze the vast data accessed by the Sentinel Initiative, identifying safety signals and trends more efficiently.
Predictive Modeling: By incorporating predictive modeling capabilities, the FDAi platform could help the Sentinel Initiative forecast potential safety issues before they become widespread, allowing for proactive regulatory actions.
Data Sharing Mechanisms: The FDAi’s focus on privacy-preserving data sharing would enable a more seamless exchange of insights between the Sentinel system and other research entities, improving the collective understanding of drug safety.

Support for the All of Us Research Program

All of Us Research Program Background: The All of Us Research Program, part of the National Institutes of Health (NIH), aims to gather health data from one million or more people living in the United States to accelerate research and improve health by taking individual differences in lifestyle, environment, and biology into account.

FDAi Integration and Support:

Voluntary Data Contribution: Participants in the All of Us Research Program could opt to share their anonymized health data or insights derived from local analysis with the FDAi platform, enriching the data available for health research.
Targeted Research Support: Insights gained from the FDAi platform could be used to inform specific research projects within the All of Us initiative, such as studies on the efficacy of personalized medicine approaches.
Collaborative Data Use: The FDAi’s emphasis on open-source development and collaborative research could foster joint projects between the All of Us program and FDA-regulated product developers, leveraging shared data to advance public health goals.

Integration with Other Government Health Initiatives

National Health and Nutrition Examination Survey (NHANES):

The FDAi could analyze NHANES data to identify broader public health trends and dietary supplement use patterns, supporting regulatory decisions and public health recommendations.

Centers for Disease Control and Prevention (CDC) Public Health Surveillance:

The FDAi platform could enhance CDC’s surveillance activities by providing AI-driven analysis of health data trends, helping to identify emerging public health threats more quickly.

National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program:

By integrating cancer statistics and research findings from the SEER program, the FDAi could assist in identifying potential correlations between drug use and cancer outcomes, supporting both regulatory actions and cancer research.

Implementation Considerations

To ensure successful integration and support of these initiatives, several key considerations must be addressed:

Data Privacy and Security: Establishing stringent data privacy and security measures is crucial, especially when integrating data from various sources.
Interoperability Standards: Developing and adhering to interoperability standards to facilitate data exchange and integration across different health data systems and initiatives.
Stakeholder Engagement: Engaging with stakeholders from each initiative to understand their data needs, challenges, and how the FDAi platform can provide the most value.
Regulatory and Ethical Compliance: Ensuring that all data sharing and analysis activities comply with relevant regulations and ethical guidelines.

By addressing these considerations and leveraging the strengths of each initiative, the FDAi platform could significantly enhance health research and public health surveillance capabilities, ultimately leading to improved health outcomes for the population.

Section 7. Cost-Savings

Research indicates that the widespread adoption of artificial intelligence (AI) in healthcare could result in significant cost savings. A study highlighted by CEPR suggests that within the next five years, utilizing current AI technology could save 5% to 10% of healthcare spending, translating to $200 to $360 billion annually. These savings are expected not to compromise the quality or accessibility of healthcare services; in fact, they might enhance the quality of healthcare experiences for patients.

This substantial reduction in healthcare spending can be attributed to several factors facilitated by AI, including improved efficiency in healthcare delivery, enhanced accuracy in diagnosis and treatment planning, reductions in unnecessary interventions, and optimized resource allocation. AI’s potential to transform healthcare through personalized medicine, early detection of diseases, and streamlined clinical trials aligns with the objectives of projects like the FDAi Act, suggesting that similar savings could be anticipated with its implementation.

Section 7. Funding

Congress shall allocate the necessary funds for the development, maintenance, and continual improvement of the FDAi Platform and its associated components.

Development Costs

AI Development and Testing

Research and development of AI algorithms: $150 million
Testing and validation: $50 million

Infrastructure and Technology

Data storage and processing infrastructure: $100 million
Cybersecurity measures: $50 million
Development of the open-source platform: $30 million

Integration with Existing Systems

EHR system integration: $75 million
Integration with wearable and monitoring devices: $50 million
Collaboration with existing health data systems (e.g., GSRS, Sentinel Initiative): $25 million

Operational Costs

Personnel

Salaries for data scientists, AI specialists, and healthcare professionals: $200 million annually
Administrative and support staff: $50 million annually

Data Acquisition and Management

Data acquisition costs: $30 million annually
Data management and privacy compliance: $20 million annually

User Support and Training

Development and delivery of training programs: $40 million
User support services: $20 million annually

Research and Development

Continuous Research and Data Analysis

Ongoing health outcome analysis and dietary effect studies: $100 million annually
Development of predictive models and clinical trial support: $75 million annually

Clinical Trial Support

Facilitating user participation in clinical trials: $50 million annually

Regulatory Compliance and Ethical Oversight

Compliance and Legal Costs

Regulatory compliance and legal consultations: $30 million annually
Ethical oversight committee operations: $10 million annually

Miscellaneous Costs

Contingency Fund

Unforeseen expenses and challenges: $50 million

Public Awareness and Engagement

Campaigns and community engagement: $20 million

Total Estimated Budget Summary

Total Development Costs: $530 million
Annual Operational Costs: $370 million (excluding continuous research funding)
Annual Research and Development Costs: $175 million
Annual Regulatory Compliance and Ethical Oversight: $40 million
Miscellaneous Costs: $70 million
Grand Total for Initial Year: $1.185 billion
Subsequent Annual Costs: $585 million (Operational, R&D, Compliance, excluding initial development costs)

Notes on the Budget

Flexibility and Scalability: The budget should be adaptable, with room for adjustments as the project progresses and as new technological advancements and partnerships emerge.
Public-Private Partnerships: To offset costs, partnerships with private entities, research institutions, and international health organizations could be pursued.
Funding Sources: Funding could come from federal allocations, grants, and potentially contributions from private sector stakeholders interested in advancing healthcare through AI.

Section 8. Effective Date

This Act shall take effect immediately upon its passage, with the initial release of the FDAi Platform scheduled for no later than two years from the date of enactment.

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