How to Evaluate AI Vendors for Public Health: A Practical Guide for Agencies

AI tools from healthcare and drug research don’t always translate to public health. Agencies must evaluate vendors carefully to ensure accuracy, fairness, and compliance with public health standards.

How the AI Vendor Landscape is Expanding in Healthcare and Public Health

Over the past decade, artificial intelligence (AI) has moved from academic prototypes to widely available commercial products. Many of the most visible AI tools have emerged in healthcare delivery and pharmaceutical research. Hospitals are using AI for diagnostic imaging, risk prediction for hospitalized patients, and automated triage. Pharmaceutical companies have adopted AI to identify potential drug targets, model molecular interactions, and accelerate clinical trial recruitment.

These tools demonstrate the potential of AI to handle complex biomedical data, operate at scale, and provide outputs that inform real-world decision-making. They also show how quickly vendors can move from a promising algorithm to a marketed product. For public health agencies, this can create the impression that solutions for their challenges are ready for purchase. In reality, many AI products designed for clinical or research settings are not optimized for the distinct data systems, work flows, and population-level goals of public health.

Evaluating whether a vendor’s AI product is truly suited for public health requires a systematic approach. Agencies must consider not only whether the tool works, but whether it works with their data, in their operational environment, and under the ethical and legal constraints that govern public health practice.

Importance of evaluating vendor products for public health

Public health decisions have wide-reaching consequences. An AI system that produces biased, inaccurate, or misinterpreted outputs can misdirect resources, delay critical interventions, and erode public trust. Vendor evaluation is therefore both a technical and a governance process. It ensures that any AI integrated into public health operations meets standards for accuracy, fairness, transparency, and security, while also aligning with the agency’s mission and capacity.

Understanding what is different about public health AI needs

Data characteristics

Public health data often come from multiple sources — laboratory reports, hospital emergency department data, syndromic surveillance, environmental monitoring, census data — and are collected under varying timeframes and formats. Clinical AI tools may be trained on standardized electronic health records that are not representative of this complexity.

Operational goals

Healthcare AI often focuses on optimizing individual patient care, while public health AI must operate at the scale of communities or populations. Predictions, classifications, or risk scores may need to be aggregated, compared across regions, and translated into policy or programmatic action.

Decision context

In public health, AI outputs inform decisions made within a political, social, and legal framework. A disease outbreak prediction impacts not only resource allocation but also public messaging, coordination with other agencies, and potential economic impacts.

Ethical oversight

Public health agencies are bound by confidentiality laws, public accountability, and equity considerations. These constraints may limit what data can be used for AI training, where it can be processed, and how outputs are disseminated.

The practice, problems, and potential of evaluating AI vendors

Current practice in public health IT

When agencies evaluate AI vendors today, the process often mirrors how they evaluate other IT systems: issuing a request for proposals, reviewing technical specifications, conducting demonstrations, and seeking references from current customers. This process works for established technologies but can be insufficient for AI, where performance depends heavily on data quality, training context, and operational fit.

Some public health agencies have likely begun to develop AI-specific procurement guidelines. These include requirements for vendors to disclose their model development process, data sources, performance metrics, and bias mitigation strategies. However, such guidelines are not yet standardized, and smaller agencies may lack the capacity to conduct rigorous evaluations.

Common Pitfalls When Evaluating AI Vendors for Public Health

1. Overlap with healthcare and research products Vendors may present AI tools designed for clinical decision support or drug discovery as suitable for public health. While the underlying algorithms may be strong, they may not generalize to population-level surveillance or intervention planning without substantial adaptation.
2. Lack of transparency Some vendors treat model architecture, training data, and feature engineering as proprietary, making it difficult for agencies to assess potential biases or limitations.
3. Limited independent validation A tool may have strong performance metrics in the vendor’s marketing materials but little to no peer-reviewed or third-party evaluation in a public health context.
4. Data integration hurdles Public health agencies may lack the infrastructure to feed local data into a vendor’s system securely and in the formats required.
5. Ethical and legal risks A vendor’s privacy policy may not meet the stricter requirements of public health law. Without careful review, an agency could inadvertently expose sensitive information or violate statutory mandates.

Potential and opportunities

1. Developing public health–specific evaluation frameworks
2. Leveraging pilot projects
3. Collaborating with other agencies
4. Encouraging vendor transparency

Key evaluation domains for AI vendors in public health

1. Technical performance
   • Accuracy and reliability: Documented performance metrics on relevant public health tasks.
   • Generalizability: Evidence the model works across different populations, geographies, and data systems.
   • Explainability: Ability to provide clear, interpretable reasoning for outputs, particularly for high-stakes decisions.
   • Robustness: Performance stability when input data are incomplete, delayed, or noisy.
2. Data requirements
   • Data types: Compatibility with surveillance, laboratory, environmental, and demographic data sources.
   • Data volume: Whether the tool can function with the quantity of data available locally.
   • Data quality tolerance: Sensitivity to errors, missing fields, or inconsistent coding.
   • Privacy compliance: Alignment with public health confidentiality statutes and data sharing agreements.
3. Validation and evidence
   • Independent evaluations: Availability of peer-reviewed studies or independent testing.
   • Operational pilots: Results from deployments in similar public health contexts.
   • Comparative performance: How the tool performs against existing methods or alternative AI models.
4. Integration and usability
   • System compatibility: Ability to interface with existing databases and reporting platforms.
   • Workflow alignment: Fit with the agency’s decision-making processes and timelines.
   • User training: Resources for onboarding and ongoing skill development.
5. Ethical and equity considerations
   • Bias detection: Methods for identifying and correcting disparities in outputs.
   • Impact assessment: Analysis of how the tool’s recommendations affect different communities.
   • Transparency: Clear documentation of model inputs, assumptions, and limitations.
6. Security and governance
   • Access controls: Tracking and auditing of who accesses the system and when.
   • Data protection: Encryption, secure storage, and controlled transmission of data.
   • Incident response: Vendor procedures for breaches, model failures, or erroneous outputs.

Lessons from healthcare and drug research AI

Healthcare and pharmaceutical AI demonstrate both the promise and the pitfalls of vendor-supplied tools. In healthcare delivery, AI has improved radiology interpretation, reduced diagnostic delays, and optimized hospital operations. In drug research, AI has shortened timelines for identifying promising compounds.

However, these successes often depend on controlled environments, standardized data, and clear outcome definitions. Public health operates in more heterogeneous and unpredictable environments. Data may be delayed or incomplete, interventions may be implemented unevenly, and outcomes may be influenced by factors far outside the model’s training data.

Some healthcare AI models have also failed when deployed beyond their original development context. This “model drift” can occur when patient populations, clinical practices, or data collection systems differ from those used in training. The same risk applies in public health if a vendor tool is adopted without local validation.

Building a diligent vendor evaluation process for AI public health tools

1. Define the problem clearly
2. Assemble a multidisciplinary evaluation team
3. Request detailed documentation
4. Conduct local testing
5. Review legal and ethical compliance
6. Negotiate transparency and audit rights
7. Plan for ongoing evaluation

Why AI Literacy Matters for Public Health Agencies

An agency with strong AI literacy is better equipped to evaluate vendors critically. Staff can interpret performance metrics, spot unrealistic claims, and identify gaps in validation. They can also communicate more effectively with vendors, ensuring that public health priorities shape the tool’s design and implementation rather than being shaped by the vendor’s marketing.

Procuring AI Tools for Public Health after Evaluation

Public health agencies have much to gain from AI, but those gains depend on careful selection and oversight of vendor-supplied tools. By developing public health–specific evaluation frameworks, insisting on transparency, and building AI literacy, agencies can ensure that AI serves as a trusted ally in protecting population health.

Healthcare and drug research provide valuable lessons, but they are not direct templates for public health adoption. Vendors must demonstrate that their tools can meet the unique data, operational, and ethical demands of public health practice. The evaluation process is helps ensure accuracy, equity, and trust, which are foundations of effective public health action.