Background
Diagnostic Access and Affordability
Limited access to affordable diagnosis is a significant barrier to disease control and equitable healthcare in low- and middle-income countries (LMICs). Nearly half of the global population lacks essential diagnostic tests, and access is almost nonexistent for up to 81% of people in the poorest settings, driving delayed or missed disease detection at scale.[1]
Diagnostics and screening underpin clinical care, surveillance, and disease control, yet many tools remain too costly, infrastructure-dependent, or operationally complex for use. The World Health Organization’s (WHO) ASSURED principles, as well as the experience with malaria rapid diagnostic tests (RDTs) and community antigen testing during COVID-19, demonstrate how decentralization, low complexity, and affordability can expand reach and strengthen surveillance.[2]-[3] Where mature, widely deployed diagnostic classes already meet programmatic needs, pathway-changing innovation is more likely to yield impact than incremental improvements to established formats.
Beyond lowering consumable costs, the economic model must support high-volume screening through very low per-test costs and rapid throughput. Durable device- or platform-based solutions that use minimal or no consumables and amortize capital costs across large volumes, enabling near-zero incremental cost per test, can be transformational. Such approaches may draw on cross-sector technologies including imaging, acoustics, breath or environmental analyzers, contactless physiological monitors, or modular hardware with interchangeable sensing or analyte capabilities and minimal consumable inputs. In some contexts, appropriate use of artificial intelligence (AI) may enhance performance, enable task-shifting, automate quality control, and reduce operator variability, supporting high‑throughput deployment in real‑world LMIC workflows.
The Challenge
This Grand Challenge seeks cost-disruptive tools for diagnosis and screening, defined as devices that amortize capital to near-zero incremental cost and consumable $1-class tests that materially reset the cost curve in LMICs while meeting real-world deployment constraints (see Table 1). For screening applications, cost targets should be interpreted per person screened; for diagnostic or monitoring applications, per test performed. Accordingly, this initiative aims to translate these cost-disruptive concepts into scalable solutions across high-priority disease areas.
We are particularly interested in transformative, high-risk, high-reward innovations that fundamentally rethink how diagnosis or screening is performed, including novel sensing modalities, software-defined diagnostics, and AI-enabled or software-only approaches that materially change performance, cost structure, or deployment models.
Table 2 outlines topic areas and use cases that are in scope for this RFP. However, applicants are not required to demonstrate existing disease-specific validation data for these use cases. Cross-sector or cross-disease innovations are explicitly encouraged. For example, platforms or technologies initially developed for non‑health, non‑diagnostic, or different disease applications are eligible, provided the proposal includes a clear, technically credible, milestone-based plan to adapt the technology to at least one relevant use case in Table 2.
The Challenge aims to:
- Source cost-disruptive devices and $1-class diagnostics for the priority conditions listed in Table 2, including cost-enabling manufacturing innovations.
- Advance cross-sector, platform, and multimodal solutions to enable scalable screening, same-visit decision-making, or reconfigurable use cases.
- Build a staged portfolio spanning high-risk early concepts to later-stage adaptation and scale, using milestone-based awards aligned to clear technology readiness level (TRL) criteria.
We are looking for proposals that:
- Clearly articulate the relevant LMIC disease focus area and intended use case from Table 2, especially transformative approaches with a technically credible adaptation pathway where disease-specific validation does not yet exist.
- Describe operational feasibility for LMIC settings (see Table 1), including explicit attention to high-throughput screening workflows, where applicable.
- Include a feasible workplan and milestones appropriate to the maturity of the technology.
- Provide clear evidence to justify the requested funding level.
- Present a credible pathway to population-scale economics (approximately US$1 or near-zero incremental cost), including key assumptions. For platform-based solutions, describe how multiple use cases can improve economics and sustainability.
- Commit to independent evaluation participation and appropriate ethical, regulatory, and Foundation open access policy.