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Striking a Balance: Innovation, Equity, and Consistency in AI Health Technologies.

Eric Perakslis^1,2, Kimberly Nolen³, Ethan Fricklas¹

¹Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States.

|July 3, 2025

Summary

Artificial intelligence (AI) innovation requires specialized regulatory frameworks, especially in biopharma. A hybridized approach is proposed to assess AI patient solutions, ensuring regulatory clarity and fostering innovation in healthcare.

Keywords:

algorithm artificial intelligence deep learning digital health health technology large language model machine learning natural language processing practical model predictive analytics predictive model predictive system regulatory regulatory landscape

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Published on: June 13, 2025

Area of Science:

Biopharmaceutical Innovation
Artificial Intelligence in Healthcare
Regulatory Science

Background:

Current AI innovations challenge existing regulatory classifications, often forcing them into unregulated wellness or consumer electronics domains.
Biopharma companies face stricter regulations (e.g., Sunshine Act) than consumer electronics, creating an uneven playing field for AI development.
Lack of regulatory clarity hinders AI adoption and innovation in healthcare, particularly for patient care solutions.

Purpose of the Study:

To review the current regulatory landscape for AI-based products, considering biopharma's existing responsibilities.
To propose a novel, hybridized regulatory framework for assessing AI-based patient solutions.
To ensure AI innovation in biopharmaceuticals is not stifled by ambiguous or inadequate regulations.

Main Methods:

Review of existing regulatory frameworks and legal responsibilities within the biopharma industry.
Analysis of current AI innovations and their classification challenges.
Development and elaboration of a proposed hybridized approach through case studies.

Main Results:

Identified significant gaps and ambiguities in current regulations for AI medical devices.
Proposed a novel hybridized framework to address the unique challenges of AI in biopharmaceutical patient solutions.
Demonstrated the feasibility and importance of this approach via case studies.

Conclusions:

A specialized, hybridized regulatory framework is essential for the safe and effective integration of AI in biopharmaceutical patient care.
This approach aims to balance regulatory oversight with the need to foster innovation and scale AI-driven healthcare solutions.
Regulatory clarity is crucial for encouraging biopharma investment in AI to improve patient outcomes.