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How AI-LiSHealth+ works

AI-LiSHealth+ workstreams (WS)

The four workstreams provide the Network's core interdisciplinary structure. They are designed to work together rather than operate as separate silos; a major challenge may draw expertise from several workstreams.

WS1 — Trustworthy AI, Health Data and Digital Innovation

Stream's main focus:

  • Trustworthy AI and responsible digital innovation
  • Health data quality, governance and cybersecurity
  • Human-centred design and technical/data readiness

Core members:
Professor Russell Beale
Dr Animesh Acharjee
Professor Sonal Choudhary

Expected contribution:
Identify AI/data challenges, assess technical and data readiness, and support responsible AI and digital health pathways.

WS2 — MedTech, BioPharma and Translational Therapeutics

Stream's main focus:

  • AI-enabled MedTech and digital health
  • Clinical and translational readiness
  • Evaluation, implementation and adoption pathways 

Core members:
Professor Alan M Jones (Lead)
Dr Animesh Acharjee
Professor Russell Beale

Expected contribution: Develop clinically relevant challenges and connect technical development with translation, evaluation and adoption.

WS3 — Prevention, Mental Health, Health Equity and Safeguarding

Stream's main focus: 

  • Prevention, mental health and health inequalities
  • PPIE, safeguarding and inclusion
  • Patient, public and community benefit

Core members:
Dr Balachandran Kumarendran (Lead)
Dr Sian Lowri Griffiths
Professor Jessica Woodhams
Professor Victoria Goodyear
Dr Laurence Lessard-Phillips

Expected contribution: Embed equity, prevention, PPIE and safeguarding within challenge development and future funding proposals.

WS4 — Economics, Finance, Procurement, Adoption and Policy Scale-up

Stream's main focus: 

  • Economic and business-case development
  • Procurement, investment and implementation
  • Commercialisation, policy and scale-up

Core members:
Professor Aditya Goenka (Lead) 
Dr Jason Lowther (Lead)
Dr Santosh Koirala

Expected contribution: Develop adoption, business-case, procurement, investment and policy pathways to support scale and sustainability.

AI-LiSHealth+ workshop series

The workshop series is a practical route for external organisations and researchers to engage with the Network. Workshops are designed around specific needs and produce tangible outputs rather than functioning only as discussion events.

Workshop series

Series Workshop Workstreams Purpose Output

1

NHS AI & Digital Health Challenge Workshop

 WS1 + WS2 + WS4

Identify NHS-defined clinical, operational and service challenges where AI, digital health or data-driven innovation could add value.

 Prioritised challenge list; 1–2 challenge briefs; partner and capability map.
2 Trustworthy AI & Health Data Workshop WS1 Build responsible AI, data-governance, trust and risk requirements into emerging health innovation opportunities from the outset. Trust/data/governance checklist; risks and dependencies; readiness actions.
3 MedTech Translation Workshop WS2 + WS1 Test how promising AI-enabled MedTech, digital-health and translational ideas can progress towards clinical evaluation, adoption and scale. Translation pathway covering evidence, users, implementation, adoption and commercial feasibility.
4 Health Equity, Prevention & PPIE Workshop WS3 Ensure prevention, mental health, safeguarding, patient/public involvement and health-equity considerations shape project design and implementation. Equity and PPIE framework; underserved-group considerations; safeguarding and inclusion actions.
5 AI Adoption, Procurement & Economics Workshop WS4 Address non-technical barriers to scale, including economic case, procurement, investment, implementation, organisational adoption and policy. Adoption and scale-up pathway; economic/procurement questions; policy and investment requirements.
6 Funding & Consortium Development Workshop All workstreams + Accelerator Convert the strongest challenges into submission-ready consortium opportunities and identify appropriate funding routes. Named consortium; funder/call fit; draft work packages; governance; outline budget; submission timetable.

Participation support: Where eligible and agreed in advance, reasonable participation-related expenses may be supported through the Network in line with University of Birmingham policies and the terms of the Network+ award.

Have a challenge we should explore?

AI-LiSHealth+ invites organisations to bring real-world questions, unmet needs, datasets or innovation challenges to the Network. The organisation does not need to arrive with a fully formed research proposal. We will work with partners to clarify the problem, identify the right expertise and determine whether a research, implementation, policy, data or funding pathway can be developed.

Who can challenge?

Challenges may come from NHS organisations, local or national government, public bodies, companies, charities, community organisations, patient groups, professional networks or international partners.

Examples of suitable challenges

  • A clinical or operational problem where AI or digital technology may add value.
  • A health-data challenge requiring better integration, analysis or governance.
  • A MedTech or digital-health innovation that needs evidence, evaluation or an adoption pathway.
  • A health-equity, prevention, mental-health or safeguarding challenge.
  • A procurement, financing, implementation or policy barrier preventing an innovation from scaling.
  • A climate, environmental-health or population-health challenge requiring interdisciplinary data and analytical expertise.

What happens after a challenge is submitted?

  1. Initial discussion to understand the problem and intended beneficiaries.
  2. Identification of relevant workstream(s), researchers and external expertise.
  3. Assessment of available evidence and data, including governance requirements.
  4. Development through a workshop, sandpit or focused challenge session where appropriate.
  5. Production of a structured Challenge Brief and a clear decision on the next route.
  6. High-readiness opportunities may progress to the Mission Translation and Consortium Accelerator.

Data collaboration and future data resource

Many organisational challenges depend on access to relevant, high-quality data. AI-LiSHealth+ is interested in working with partners who have data that could support research, evaluation, service improvement or innovation development.

Any data collaboration would be subject to appropriate ethical review, information governance, data protection, confidentiality, security and data-sharing arrangements. The Network will not assume that data can be transferred or reused without the required permissions and safeguards.

As the Network develops, we will explore the feasibility of creating a governed data resource or repository that can help participating partners and researchers identify available datasets, metadata, access conditions and opportunities for responsible interdisciplinary analysis.

What are the potential benefits of a governed data resource?

  • Improve visibility of relevant datasets and data capabilities.
  • Reduce duplication by making existing resources easier to discover.
  • Support interdisciplinary research and comparative analysis.
  • Create stronger foundations for collaborative funding applications.
  • Encourage responsible, transparent and reusable data practices.
  • Help organisations connect their data with appropriate analytical expertise.

Meet the team

Network Lead

Dr Anandadeep Mandal

Workstream leadership

The Network is also developing an External Advisory Forum to bring external challenge, implementation experience and partner perspectives into the programme.