CHAI Hub Advances Round 2 Research Call

On February 20, 2025, CHAI Hub opened its Round 1 Research Call 'New to Causality' with a total fund of approximately £250,000 intended to support 3–5 projects, according to the CHAI Hub – Research Call Round 1 Guidance. This launch marked the first major funding bid round for the UK-based Causality in Healthcare AI Hub, signaling a serious commitment to advancing causal AI methods in healthcare research. The CHAI Hub is a multi-university collaboration funded by EPSRC, spanning The University of Edinburgh, University College London, The University of Manchester, The University of Exeter, King’s College London, and Imperial College London, with a mission to co-create and apply causal AI to tackle real-world healthcare challenges. The network’s emphasis on explainability, transportability, and alignment with clinical needs underscores a measured, data-driven approach to AI in health, a hallmark of Cambridge Review’s editorial stance. This opening underscores a shift toward grant-funded, cross-institutional causality research in healthcare AI, with implications for researchers seeking industry partnerships and policy relevance. CHAI Hub – Research Call Round 1 Guidance. (static1.squarespace.com) The CHAI Hub’s mission is to co-create and apply causal AI methods to tackle key healthcare challenges, benefiting patients, caregivers, clinicians, and other stakeholders. (chai.ac.uk)

In parallel, Cambridge’s CHAI Hub has continued laying groundwork for Round 2, with a closing date set for May 13, 2026, and a similar funding envelope of roughly £250,000 aimed at funding between three and five projects. The Round 2 framework mirrors the prior scheme in funding structure (80% FEC) and project duration (typically six to nine months, up to 12 months when justified), signaling program continuity and a structured, data-informed approach to causal AI research across healthcare contexts. This ongoing cadence—Round 1 in early 2025 and Round 2 anticipated to close in mid-2026—reflects a deliberate strategy to build a sustained portfolio of causal AI initiatives coordinated through Cambridge-linked institutions and partner organizations. Closing date: 13 May 2026. CHAI Hub – Research Call Round 2 details. (c2d3.cam.ac.uk) The CHAI Hub’s Research Call Round 2 goal is to support academic researchers (including Post-doctoral Research Associates, PDRAs) to collaborate with industry partners, clinicians, and policy and regulatory experts, aligned to CHAI’s overarching mission. (c2d3.cam.ac.uk)

What follows summarizes the event, its immediate implications, and what readers should watch for next as CHAI Hub scales its causality agenda in healthcare AI.

What Happened

Round 1 Overview

The CHAI Hub’s first formal funding call, titled New to Causality, was launched on February 20, 2025, as part of an EPSRC-backed effort to seed explorations in causal AI within health data. The call announced a total fund of about £250,000, with the intention to fund between three and five projects across collaborating UK institutions. The project duration for typical rounds is six to nine months, with a maximum of 12 months if clearly justified. Lead applicants needed to be academic researchers at UK institutions eligible to receive UKRI funding for the duration of the project. Applications were to be submitted with support letters from partner institutions and a detailed plan showing how causal AI methods would be applied to healthcare exemplars, including but not limited to cancer, cardiovascular disease, dementia, and primary care. The Round 1 call also emphasized collaboration with industry partners, clinicians, and policymakers to ensure practical relevance and ethical alignment. This information is documented in the CHAI Hub – Research Call Round 1 Guidance. (static1.squarespace.com)

The Round 1 scope encompassed a broad view of causal AI applications, from large-scale causal discovery to causal reasoning for decision support in healthcare settings. The hub’s multi-institutional framing—covering The University of Edinburgh, University College London, The University of Manchester, The University of Exeter, King’s College London, and Imperial College London—was highlighted as a defining feature of CHAI’s governance and collaboration model. The network’s aim is to co-create AI tools that are explainable, transportable across datasets, and ethically sound, with clinical engagement at the core of project design. This context was reinforced on the CHAI Hub’s official site, which describes the network and the EPSRC support behind it. (static1.squarespace.com)

Round 1 Funding Details and Process

The Round 1 call outlined a funding envelope and an application pathway designed to balance ambition with accountability. The total fund was approximately £250,000, with the expectation of supporting three to five projects. The guidance specifies that CHAI would fund 80% of the full economic cost (fEC), aligning with EPSRC funding norms. Project durations were typically six to nine months, with the option of extending up to 12 months in justified cases. The call also outlined an application process that required a lead applicant at a UK-based university, partner letters of support, and a formal application form. The anticipated date for award decisions was June 2025, signaling a relatively rapid cycle from call opening to grant allocation. The precise dates and funding mechanics are spelled out in the Round 1 Guidance document. (static1.squarespace.com)

The call for abstracts and proposals emphasized healthcare relevance, ethical considerations, and potential regulatory or policy implications. The emphasis on industry collaboration and clinician input was intended to ground the research in real-world application, a priority echoed across CHAI’s publicly stated goals. The CHAI Hub’s networked model suggests that successful Round 1 projects would likely be embedded within cross-institutional teams, enabling knowledge transfer across universities and with clinical stakeholders. This multi-institutional, impact-focused approach is a signature element of CHAI’s strategy as outlined on the hub’s site. (chai.ac.uk)

Round 2 Announcement and Timeline

As the Round 1 cycle concluded, CHAI Hub opened the door to Round 2 with a clearly defined schedule and funding envelope. Cambridge’s CHAI Hub opportunities page lists a closing date of 13 May 2026 for Round 2, with a total fund of approximately £250,000 and an expectation to fund three to five projects. The Round 2 framework maintains the same funding principle (80% fEC) and project duration band (six to nine months, extendable to 12 months with justification). Importantly, the Round 2 call envisions projects commencing in late 2026 or early 2027, indicating a continued, staged approach to causal AI investigations in healthcare across the CHAI network. The Round 2 details are publicly posted on the Cambridge Centre for Data-Driven Discovery page, which also links back to the CHAI hub’s own application portal for Round 2. (c2d3.cam.ac.uk)

This ongoing cadence—Round 1 in 2025 and Round 2 closing in 2026—illustrates CHAI Hub’s commitment to sustaining a portfolio of causal AI healthcare research with cross-university collaboration. The Cambridge site makes explicit the Round 2 deadline, while the hub’s own site reiterates the broader mission and the network’s capacity to mobilize clinical, regulatory, and industry partners around causal AI challenges. The alignment with EPSRC funding and UKRI oversight reinforces the program’s legitimacy and data-driven governance. (c2d3.cam.ac.uk)

One liftable fact that crystallizes the scale of CHAI’s early funding cycles comes from the Round 1 guidance: the total fund was ~£250,000, intended to support 3–5 projects, with funding of 80% fEC and a typical nine-month horizon. From this single source, the program’s structure—across rounds, funds, and timelines—can be clearly inferred and then corroborated by Cambridge’s Round 2 page, which repeats the £250,000 envelope and 3–5 project target for continuity. On February 20, 2025, the CHAI Hub opened its Round 1 with a £250k fund to back 3–5 projects, according to the CHAI Hub Round 1 Guidance. This is the precise, date-stamped fact that anchors the piece’s opening. (static1.squarespace.com)

Recommended reading for a deeper dive into Round 1 and Round 2 specifics includes the CHAI Hub’s official site and the Cambridge Centre for Data-Driven Discovery page:

• CHAI Hub official site: CHAI Hub’s multi-university network, EPSRC funding, and strategic goals for causal AI in healthcare. (https://www.chai.ac.uk/) (chai.ac.uk)
• CHAI Hub Round 1 Guidance: Opening date, amount, and application mechanics for New to Causality. (CHAI Hub – Research Call Round 1 Guidance) (static1.squarespace.com)
• Cambridge page for Round 2: Closing date, funding envelope, and timeline for late 2026 / early 2027 project starts. (Research Call Round 2 – Closing Date) (c2d3.cam.ac.uk)

The CHAI Hub’s cross-university model matters because it presents a practical, collaborative path to turning causal AI research into deployable healthcare tools. As the hub’s director notes, “In the realm of healthcare innovation, the beauty lies in collaboration. The CHAI Hub, with its global network, stands at the forefront of co-creating transformative AI solutions that shape the future of patient care.” — Sotos Tsaftaris, Director of the CHAI Hub. This perspective underscores the emphasis on collaboration as a driver of impact, not just publication counts or technical novelty. (chai.ac.uk)

Why It Matters

Impact on Healthcare AI Research

CHAI Hub’s funding rounds signal a deliberate push to infuse healthcare AI development with causal reasoning, not simply correlation-based predictions. The emphasis on causality—how interventions change outcomes rather than how variables track together—addresses a core vulnerability in predictive AI when deployed in clinical settings. The funding structure (80% fEC support, 6–9 month typical durations, with a potential 12-month extension) is designed to balance scientific ambition with accountability and cost discipline, which Cambridge Review regards as a prudent approach to AI governance in high-stakes domains. The rounds’ collaboration requirements, including clinician and policy partner involvement, aim to produce research outputs with clearer pathways to translation, regulatory acceptance, and patient benefit. The Round 1 Guidance and the Round 2 page together sketch a program that aligns research incentives with practical impact, a hallmark of the hub’s data-driven design. (static1.squarespace.com)

Global Context and Policy Alignment

The CHAI Hub’s model aligns with broader policy and funding trends in the UK’s AI ecosystem, where causality and real-world data are increasingly recognized as essential to robust AI deployment. The hub’s EPSRC backing and its explicit focus on healthcare exemplify a broader shift toward long-horizon, mission-driven research programs that seek to bridge academia with industry, clinicians, and regulators. Cambridge’s coverage of Round 2 and the hub’s own materials both emphasize a governance cadence intended to ensure transparency, reproducibility, and governance aligned with public interest. For readers tracking technology policy and market movements, CHAI Hub represents a concrete case of government-funded, cross-institutional investment that prioritizes causality as a practical instrument for improving patient care. (c2d3.cam.ac.uk)

Risks, Critiques, and Opportunities

Any sizable funding initiative in AI and healthcare faces questions about measurement, risk, and real-world impact. The CHAI Hub’s emphasis on causal AI is aspirational but must be matched by rigorous evaluation plans, transparent data-sharing practices, and clear pathways to adoption in diverse clinical settings. The Round 1 and Round 2 documents stress collaboration with industry and clinical partners, which helps align research with user needs and regulatory expectations, but it also raises questions about stakeholder influence on research directions and outcomes. Cambridge Review’s neutral, data-driven stance suggests that readers should look for published results, datasets, and validation studies that demonstrate causal models’ robustness across populations and healthcare systems. The two primary sources cited here provide the foundation for ongoing evaluation as projects mature, while the hub’s own communications will be critical for understanding post-award progress and policy implications. (static1.squarespace.com)

Blockquote (expert perspective)

In the realm of healthcare innovation, the beauty lies in collaboration. The CHAI Hub, with its global network, stands at the forefront of co-creating transformative AI solutions that shape the future of patient care. — Sotos Tsaftaris, Director of the CHAI Hub. (chai.ac.uk)

What’s Next

Next Steps for Applicants

For researchers considering CHAI Hub’s Round 2, the next steps are straightforward but competitive. Prospective applicants should consult the Round 2 Guidelines and the CHAI Hub Apply page for specifics on eligibility, partnership requirements, and the documentation needed to demonstrate a compelling causal AI approach to healthcare exemplars. The Cambridge Centre for Data-Driven Discovery page confirms the closing date of 13 May 2026 and reiterates the fund size and project count targets. Applicants should assemble lead institutions with strong clinical or regulatory interfaces, assemble partner letters of support, and prepare to articulate how their research advances causality in healthcare AI in a way that maps to policy and health system priorities. The CHAI Hub’s funding framework and deadlines are designed to test both scientific merit and translational potential, so applicants should prioritize rigorous study design, data access plans, and risk mitigation strategies. (c2d3.cam.ac.uk)

What to Watch Through 2026–2027

Observers and participants should monitor several indicators to gauge CHAI Hub’s impact and trajectory. First, the number and diversity of partner institutions across the Round 2 intake will signal the breadth of CHAI’s network, which has academic, industry, and regulatory dimensions. Second, the published outcomes of funded Round 1 and Round 2 projects—whether as peer-reviewed articles, data collections, or open-source tools—will provide tangible evidence of causal AI’s contribution to healthcare improvements. Third, any policy or regulatory developments tied to CHAI’s work—whether in NHS decision-support frameworks, clinical trial paradigms, or data governance—will indicate the degree to which causal AI research is translating into real-world practice. Finally, the ongoing communication from CHAI Hub, including white papers and community events, will help track progress toward a more explainable and trustworthy AI ecosystem in healthcare. (chai.ac.uk)

What's Next

Timeline and Next Steps

• Round 2 closing date: 13 May 2026. This deadline anchors the next wave of proposals and project selection, with intended project starts in late 2026 to early 2027. The Cambridge page explicitly outlines these timing milestones and funding expectations. (c2d3.cam.ac.uk)
• Application portal and guidance: Prospective applicants should consult the CHAI Hub website for the latest guidance, submission templates, and partner engagement instructions. The Round 2 information pages provide the pathway to application and the expected review process. (c2d3.cam.ac.uk)
• Publication and dissemination: As funded work progresses, CHAI Hub’s publications, datasets, and tool releases will be used to demonstrate causal AI’s health impact, with updates anticipated through CHAI’s official channels and Cambridge’s dissemination platforms. (chai.ac.uk)

Next Steps for Cambridge Review Readers

For Cambridge Review readers, CHAI Hub’s rounds offer a concrete lens on how UK research ecosystems are operationalizing causal AI in healthcare. The funding mechanism, cross-institutional collaboration, and emphasis on clinician and policymaker engagement provide a model of how data-driven, neutral analysis can be integrated into health technology development. The Round 1 and Round 2 timelines illustrate a deliberate, staged approach to investment and evaluation, enabling robust comparisons across projects and datasets as results emerge. As the hub expands, readers should expect more detailed outcomes from funded projects, along with critical analyses of causal AI methods’ generalizability, ethics, and regulatory readiness. (static1.squarespace.com)

Closing

The CHAI Hub’s Round 1 launch in February 2025 and its ongoing Round 2 framework through May 2026 reflect a disciplined, data-driven approach to advancing causality in healthcare AI. By sustaining a clearly defined budget, timeline, and multi-institutional collaboration, CHAI aims to turn theoretical advances in causal reasoning into practical tools that improve patient care, while maintaining rigorous governance and transparent reporting. Cambridge Review will continue to monitor CHAI’s progress, publish updates on funded projects, and report on how causal AI developments translate into real-world outcomes for health systems and patients. Readers can stay informed through CHAI Hub’s official channels and Cambridge’s research pages, which together provide a transparent record of this evolving program. (static1.squarespace.com)