Oxford Pilots TrustedMDT: Multi-Agent AI Integrated Into Microsoft Teams To Support Cancer Treatment Planning
University of Oxford unveiled TrustedMDT, a multi-agent AI system designed to help medical specialists throughout most cancers remedy planning conferences.
Developed in collaboration with expertise firm Microsoft, the AI device has been built-in into Microsoft Teams and will probably be piloted at Oxford University Hospitals NHS Foundation Trust, representing one of many first makes use of of agentic AI in a clinically real looking tumour board atmosphere.
Multidisciplinary Tumour Board conferences within the UK deliver collectively radiologists, pathologists, surgeons, and oncologists to evaluation diagnostic outcomes and develop remedy plans. Rising caseloads, nevertheless, are putting rising stress on skilled capability.
Research from Cancer Research UK signifies that groups usually spend lower than two minutes discussing every affected person, with vital info gaps contributing to delays in 7% of circumstances, which might impression remedy timelines, analysis alternatives, and clinician workload.
TrustedMDT was designed to handle these pressures by automating information synthesis and evaluation via three coordinated AI brokers.
The Clinical Summarisation Agent evaluations digital well being information—together with radiology, pathology, and biomarker information—to generate concise tumour-specific summaries. The Cancer Staging Agent evaluates illness development utilizing worldwide staging requirements, whereas the Treatment Planning Agent produces evidence-based remedy suggestions aligned with skilled tips.
Together, these brokers intention to reinforce the effectivity and accuracy of tumour board decision-making.
Oxford University Hospitals Pilots TrustedMDT, Assisting Oncology Tumour Board Decisions
Dr. Andrew Soltan, Lead Investigator and Specialty Registrar in Medical Oncology at Oxford University Hospitals, defined that conventional chatbots are inadequate for the complexity of oncology, prompting the event of a hierarchical multi-agent system. In this structure, every agent includes sub-agents centered on particular information units and geared up with related instruments, requiring the system to purpose via medical tips and cross-check suggestions towards affected person histories to cut back errors.
The Oxford group deployed these customized brokers inside Microsoft Teams utilizing the healthcare agent orchestrator, integrating the AI instantly into current multidisciplinary tumour board workflows.
Dr. Soltan emphasised that the system is designed to help medical processes with out disruption, functioning as a ‘digital collaborator’ that permits clinicians to offer enter in actual time and evaluation the rationale behind AI-generated suggestions, with last selections remaining beneath human management.
Oxford University Hospitals has acquired approval to conduct a two-phase pilot examine to guage TrustedMDT’s accuracy, usability, and technical efficiency. The first part benchmarks AI outputs towards skilled selections utilizing anonymized most cancers circumstances, whereas the second part simulates tumour board conferences to evaluate how successfully the system summarizes info, helps dialogue, and drafts remedy plans inside real looking medical workflows. Clinical help is offered by OUH resident docs.
Dr. Ben Attwood, Chief Digital Officer at OUH, famous that the hospital is dedicated to exploring improvements that improve MDT preparation and operations whereas adhering to established governance and data safety requirements.
David Ardman, Corporate Vice President of Microsoft Health and Life Sciences, described the multi-agent system as a novel strategy in healthcare AI, enabling clinicians to work together dynamically with specialised brokers inside Teams to cut back cognitive load and enhance resolution help.
If validated, TrustedMDT might enhance communication amongst specialists, shorten remedy timelines, and broaden entry to medical trials. The pilot examine represents an preliminary step towards demonstrating the system’s potential, producing proof to tell additional technical improvement and information future, larger-scale evaluations earlier than medical deployment.
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