AI for Health: Landscape review to identify promising areas for impact

By Rethink Priorities, Ruby Dickson, Jenny Kudmowa, Aisling Leow @ 2026-02-13T16:09 (+31)

This is a linkpost to https://rethinkpriorities.org/research-area/ai-for-health/

Editorial note

This report was commissioned by Coefficient Giving (formerly Open Philanthropy) and produced by Rethink Priorities from October to November 2025. We revised the report for publication. Coefficient Giving, our expert informants, and their affiliated organizations do not necessarily endorse our conclusions.

In this report, we conducted a rapid landscape scan of AI for health applications to identify areas that appear most promising for further investigation and potential philanthropic support.  This work was informed by desk research, selective literature review, and interviews with three experts, two of whom agreed to be named.

We tried to flag major sources of uncertainty in the report and are open to revising our views based on new information or further research

Executive summary

What we did

The goal of this project was to identify promising AI for health interventions and assess their potential to generate meaningful health impact, with a focus on organizations deploying frontier AI models in real-world settings. Organizations focused mainly on R&D were out of scope. Our work combined a broad landscape scan, a rough cost-effectiveness assessment, and qualitative investigation of selected organizations. The process involved three main steps.

1. Landscape sourcing and prioritization

Over roughly one week, we built a longlist of 258 organizations using AI to improve clinical support, patient support, health operations, or population health. Each organization was screened to verify that AI was a core component of the intervention, since many groups market themselves as AI-enabled while in practice using limited AI. We then classified organizations by likely technical sophistication. Based on public information, just under half appeared to be using frontier AI models as of late 2025.

2. Assessment of intervention pathways and cost-effectiveness potential

We identified eight impact pathways through which AI could generate health value. From these, we prioritized the first six pathways for deeper analysis, focusing on those with the most direct and measurable links to near-term health outcomes: diagnostic assistance, disease surveillance, clinical skills and decision support, product safety and quality, service delivery efficiency, and patient behavior. Data-driven planning and administrative efficiency were also considered but not prioritized for cost-effectiveness analysis given their more indirect links to health outcomes and greater measurement challenges.

For selected organizations operating within the six prioritized pathways, we developed simplified, intentionally rough cost-effectiveness models designed to provide directional insight rather than precise estimates. These models aimed to surface key drivers of value, explore where more advanced AI capabilities might plausibly shift outcomes, and assess the strength and limitations of the existing evidence. Across the landscape, robust outcome measurement paired with frontier AI applications was uncommon, limiting the precision and certainty of these assessments.

3. Illustrative profiles of selected organizations

We include qualitative profiles that illustrate how selected organizations apply frontier AI within impact pathways that emerged as potentially promising.

Key takeaways

General findings:

• Frontier AI organizations are most concentrated in high-income countries. Only about one third of the frontier-AI organizations that we identified are active in low- and middle-income countries (LMICs), while simpler AI applications remain more common in resource-constrained settings.
• Frontier AI is most prominent in clinical support. Diagnostic assistance and clinical decision support tools show the highest adoption of frontier models. Patient support and population health tools more often rely on conventional machine learning.
• Our exploratory cost-effectiveness estimates varied by more than three orders of magnitude, which points to substantial dispersion in expected impact across the landscape.
• Organizations operating in LMICs tend to show the strongest promise. Higher disease burden, larger access gaps, and greater marginal returns to improved service quality contribute to stronger apparent cost-effectiveness.
• Organizations focusing on diagnostic assistance, disease surveillance, clinical skills and decision support, and product safety and quality show the strongest potential. Based on the initial outputs from our analysis, tools that can improve both service quality and efficiency, stop disease outbreaks earlier, or reduce exposure to substandard care can have outsized impact, particularly in low-income contexts.
• Organizations focusing on service delivery efficiency and patient behavior tools appear weaker. Efficiency-focused tools often struggle to translate time savings into meaningful health gains unless deployed in strongly capacity-constrained settings. Behavior change tools show measurable effects but tend to be relatively costly per user and have limited persistence of impact.
• Improvements in throughput, not accuracy, often drive impact. For many diagnostic and decision support interventions, modeled value arises primarily from increased patient volume, not higher diagnostic sensitivity.
• Gains in diagnostic accuracy come from both reducing false positives and false negatives. In some contexts, reducing false positives can significantly lower costs and resource strain– an underrecognized pathway to system efficiency for diagnostic and decision support interventions.

Limitations, and uncertainties:

• Frontier AI tools rarely align with strong evidence. Tools near the AI frontier are often early-stage and supported by limited data, while better-studied interventions tend to rely on older AI methods.
• Unclear how much AI contributes to observed effects. In most interventions, AI is part of a broader package involving hardware, workflow redesign, and training. We did not attempt to attribute impacts specifically to AI.
• Findings stem from exploratory modeling and limited evidence. Because the underlying data and assumptions vary in quality, even modest changes can alter the expected impact of an intervention. These assessments are best understood as preliminary guidance for future investigation.

Table 1: Summary of key findings on AI for health impact pathways