Designing Modular Systems for Rigorous Healthcare AI Validation
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- Gigi Yuen-Reed, PhD, the chief data and artificial intelligence officer at Cohere Health, earned a place on the 100 Women in AI 2026 list.
- General-purpose computer models often miss critical context, such as confusing a medical abbreviation with an unrelated meaning from another field.
- Real-world medical systems use evaluation-driven development, which continuously tests technology against verified clinical criteria.
- Collaborative teams of engineers and doctors build modular software that supports medical professionals instead of replacing them.
- Directing focus toward clinical safety and thorough data tracking enables health plans to achieve better results and faster care approvals.
Table of Contents
- The Standard for Healthcare AI Validation
- Understanding General-Purpose Model Limits in Medicine
- The Tech-Clinical Integration Gap
- Fulfilling the Prior Authorization Research Gap
- Closing Thoughts
The Standard for Healthcare AI Validation
Gigi Yuen-Reed, PhD, the chief data and artificial intelligence officer at Cohere Health, recently joined the 100 Women in AI 2026 list. This honor recognizes her work in building precise systems for healthcare AI validation.
Cohere Health develops clinical intelligence solutions, software applications that analyze medical data to improve decisions. These systems enable health plans and healthcare providers to work together. Her selection to this list shows the growing need for strict, evaluation-driven safety standards in medical technology.
Yuen-Reed possesses more than 20 years of experience driving technology development across healthcare and enterprise systems. She previously worked as a Distinguished Engineer at IBM and led research and development for IBM Watson Health’s $100 million analytics portfolio.
She has authored more than 15 peer-reviewed publications and holds nearly 20 patents. This deep experience guides her current work in establishing safe, clinically grounded computer systems.
Quick Take & Core Data:
- Prior authorization processes consume an estimated $35 billion in yearly administrative spending across the United States healthcare sector [1].
- Standard general-purpose models show error rates as high as 20.7% in certain prior authorization letter categories [3].
Understanding General-Purpose Model Limits in Medicine
Many technology companies are testing general-purpose large language models, computer programs trained on broad datasets to write text. Yuen-Reed argues that generic models lack the clinical precision required for medical tasks.
“General-purpose and large LLMs without domain-specific development aren’t precise or safe enough for clinical decision-making in healthcare,” she says. “Context and specificity matter heavily. When AML means something entirely different in finance than in medicine, your AI has to know the difference.”
Generic models can also exhibit stochastic behavior, random and unpredictable outputs. This behavior presents safety risks in healthcare.
Recent studies show that generic language models have a 3.5% error rate when drafting prior authorization letters [3]. Some drafts cite completely unrelated clinical trials. A prior authorization letter built on the wrong evidence can delay care or trigger a wrongful denial.
“We’re driving value by harnessing the ability of technology to grasp complex connections among symptoms, diseases, treatments, and evidence-based policies, and reason over and extract insights from structured and unstructured records,” Yuen-Reed notes.
Her work focuses on building domain-specific AI, software systems designed and trained for a single specialized industry. These systems analyze both structured medical codes and unstructured clinician narratives to extract accurate summaries.
The Tech-Clinical Integration Gap
Building safe healthcare tools requires close cooperation between software engineers and medical doctors. Yuen-Reed establishes an evaluation-driven culture that subjects every algorithm to real-world medical guidelines.
“Evaluation-driven development means AI models are continually tested and validated against real-world clinical standards, not just technical performance,” she explains.
Her engineering team works directly with active clinicians to gather feedback. This prevents engineers from designing software in isolation.
“Our engineers and scientists work closely with clinicians using our technology, so they see it in action and get ongoing feedback to continually improve it,” Yuen-Reed says. “We focus on quality over quantity.”
This collaborative design reduces the risk of automation bias, the human tendency to trust computer outputs without verifying them. Cohere Health designs modular components, software modules that perform specific tasks under human control. These components keep clinicians in charge of final approvals.
“Clinical complexity is rising, while the workforce is stretched thin,” Yuen-Reed says. “Great technology makes humans better, not replaceable.”
The development team trains these systems using real-world medical datasets. This training helps the software understand complex clinical settings and respect professional boundaries.
Fulfilling the Prior Authorization Research Gap
Historically, health systems managed prior authorization, a coverage approval process required before patients get treatments. They used manual paperwork or rigid electronic rules [2].
These older approaches could not read unstructured clinical narratives. Doctors had to spend hours copy-pasting data, leading to professional burnout [1].
Prior research proposed that a more human-like AI approach to prior authorization review could replace rigid, rules-based automation [2]. Yuen-Reed’s platform builds on that idea: it cross-references insurer policy criteria against a patient’s clinical record to surface the specific evidence a reviewer needs. This capability allows health plans to automate routine reviews while keeping complex cases under human supervision.
A main limitation of this approach is that insurer policies and clinical guidelines change constantly. Developers must continually update their clinical datasets to ensure the software remains accurate over time. Federal oversight of clinical AI, including the FDA’s software-as-a-medical-device framework, is also evolving alongside this technology.
Despite these operational challenges, the system successfully connects multiple healthcare departments.
“We’re building enterprise-wide clinical intelligence to connect functions from care planning to care management to payment and quality/population health,” Yuen-Reed says. “The value compounds over time, allowing clinicians to operate at the top of their licenses and patients to have access to the right care at the right time.”
Closing Thoughts
Strict validation of healthcare AI is essential for protecting patient safety. By keeping human clinicians at the center of the technology, health plans can reduce paperwork burdens while maintaining high standards of care.
As the medical sector continues to adopt digital tools, Gigi Yuen-Reed’s leadership shows how rigorous testing can build trust between providers, insurers, and patients.
Learn more about Gigi Yuen-Reed and Cohere Health here.
References
[1] Sahni, N. R., Istvan, B., Stafford, C., & Cutler, D. (2024). Perceptions of prior authorization burden and solutions. Health Affairs Scholar, 2(9), qxae096. https://doi.org/10.1093/haschl/qxae096
[2] Lenert, L. A., Lane, S., & Wehbe, R. (2023). Could an artificial intelligence approach to prior authorization be more human? Journal of the American Medical Informatics Association, 30(5), 989–994. https://doi.org/10.1093/jamia/ocad016
[3] Aiumtrakul, N., Thongprayoon, C., Kookanok, C., Poochanasri, M., Phichedwanichskul, K., & Cheungpasitporn, W. (2026). Quality assessment of large language model–generated prior authorization letters in nephrology. Frontiers in Digital Health, 8, Article 1767648. https://doi.org/10.3389/fdgth.2026.1767648
