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Brendan Delaney

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

AIIM Journal 2026 Journal Article

Learning Health Systems provide a glide path to safe landing for AI in health

  • Vasa Curcin
  • Brendan Delaney
  • Ahmad Alkhatib
  • Neil Cockburn
  • Olivia Dann
  • Olga Kostopoulou
  • Daniel Leightley
  • Matthew Maddocks

Artificial Intelligence (AI) holds significant promise for healthcare but often struggles to transition from development to clinical integration. This paper argues that Learning Health Systems (LHS)-socio-technical ecosystems designed for continuous data-driven improvement-provide a potential "glide path" for safe, sustainable AI deployment. Just as modern aviation depends on instrument landing systems, the safe and effective integration of AI into healthcare requires the socio-technical infrastructure of LHSs, that enable iterative development and monitoring of AI tools, integrating clinical, technical, and ethical considerations through stakeholder collaboration. They address key challenges in AI implementation, including model generalizability, workflow integration, and transparency, by embedding co-creation, real-world evaluation, and continuous learning into care processes. Unlike static deployments, LHSs support the dynamic evolution of AI systems, incorporating feedback and recalibration to mitigate performance drift and bias. Moreover, they embed governance and regulatory functions-clarifying accountability, supporting data and model provenance, and upholding FAIR (Findable, Accessible, Interoperable, Reusable) principles. LHSs also promote "human-in-the-loop" safety through structured studies of human-AI interaction and shared decision-making. The paper outlines practical steps to align AI with LHS frameworks, including investment in data infrastructure, continuous model monitoring, and fostering a learning culture. Embedding AI in LHSs transforms implementation from a one-time event into a sustained, evidence-based learning process that aligns innovation with clinical realities, ultimately advancing patient care, health equity, and system resilience. The arguments build on insights from an international workshop hosted in 2025, offering a strategic vision for the future of AI in healthcare.

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ECAI Conference 2014 Conference Paper

Information-based Incentivisation when Rewards are Inadequate

  • Samhar Mahmoud
  • Lina Barakat
  • Simon Miles
  • Adel Taweel
  • Brendan Delaney
  • Michael Luck

In many cases, intermediaries play a major role in linking between service providers and their target users. Yet, attracting intermediaries at a marketplace to promote a service to their existing customers can be very challenging, since they are usually very busy and would incur additional cost as a result of such promotion. In response, this paper presents an information-based incentivisation framework, which combines financial rewards with other motivating information, in order to incentivise intermediaries at a marketplace to undertake service promotion. Specifically, the intermediaries are associated with a group of incentivising agents, capable of learning the individual motivational needs of these intermediaries, and accordingly target them with the most effective incentives. The incentivising agents collaborate with each other to gather motivational information, by sharing their observations on intermediaries. The proposed incentivisation approach is evaluated through a corresponding agent-based simulation, and the experimental results obtained demonstrate its effectiveness.

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v2026.09.13