Our Research Agenda: AI and Digital Innovation in Upper-Extremity Care
20 August 2026 · Researchers & Professionals
hand2shoulderclinic.ai exists alongside an active clinical research effort. This page outlines our current priorities — not as finished findings, but as a transparent account of what we're working on.
Current focus areas
Outcome prediction modelling. Building on structured, de-identified clinical and outcome data from routine practice to explore whether early post-operative signals (imaging, motion tracking, patient-reported outcomes) can help predict recovery trajectory after common upper-limb procedures.
3D-planning accuracy. Comparing achieved correction against planned correction in 3D-guided osteotomy cases, to build an honest, practice-specific evidence base rather than relying solely on published series from other centres.
Digital rehabilitation adherence. Studying how app-guided, remotely-monitored rehabilitation programmes affect adherence and functional recovery compared with standard clinic-based follow-up alone.
Case-based learning resources. Structured case series and technique documentation intended for the Professional Education section of this platform, contributing to surgical education beyond our own practice.
Our approach to evidence
We aim to distinguish clearly between:
- Established practice, supported by strong published evidence
- Emerging technique, supported by early or practice-specific data, used with appropriate patient discussion
- Investigational, used only within a defined research or audit framework
This distinction is maintained consistently across the AI & Innovation Hub and Professional Education content on this site.
Collaboration
We welcome academic collaboration — case-sharing, multi-centre data pooling, or joint publication — from surgeons and researchers working in related areas. See the Contact page to get in touch.