Close the loop on radiology follow-ups
RadAssist AI helps hospitals turn radiology follow-up recommendations into clear, trackable actions with ownership, due dates, and audit trails — reducing the risk of missed or delayed patient care.
CT Abdomen & Pelvis — 14 Jan 2025
Findings: Small hepatic lesion identified at segment VI measuring 8mm...
“Repeat CT in 3 months to exclude malignancy.”
No acute findings. Recommend routine follow-up as indicated.
The follow-up gap after the report is signed off
Radiologists recommend repeat scans, clinic reviews, urgent referrals and time-bound actions every day — but many hospitals still track them through spreadsheets, emails, clinic letters or fragmented local lists.
Fragmented tracking
Follow-up recommendations are scattered across spreadsheets, email threads, clinic letters and disconnected local lists. No single view of what needs to happen and when.
Unclear ownership
It is rarely obvious who is responsible for acting on a follow-up recommendation after the report is signed off. Actions fall between departments with no named owner.
Delayed or missed actions
Without structured tracking and due dates, time-bound recommendations — repeat scans, two-week-wait referrals, critical handovers — can be missed or significantly delayed.
Weak governance visibility
Governance and clinical leads have no reliable way to see how many follow-ups are outstanding, overdue or at risk. Audit trails are incomplete and reporting is difficult.
RadAssist AI creates a dedicated follow-up workflow layer
RadAssist AI reads final report text, detects follow-up recommendations, and turns them into structured tasks that can be tracked, completed and reviewed safely.
Recommendation detection
RadAssist AI reads the text of final radiology reports and identifies follow-up recommendations — repeat scans, referrals, clinic reviews, critical findings — in a structured, consistent way.
Structured task creation
Each detected recommendation is converted into a trackable task with a recommendation type, due date, risk level and assigned owner — removing ambiguity about what needs to happen.
Shared worklist
All active follow-up tasks are visible in a shared worklist, accessible to the teams responsible for coordinating and completing them, with clear status, priority and ownership at a glance.
Governance and audit trail
Every action taken on a follow-up task is logged with a timestamp and user record. Leads can see on-time rates, overdue counts and task histories — supporting safe, auditable governance.
Important: RadAssist AI does not interpret images or make clinical decisions. It supports workflow, safety and governance after reporting.
Where RadAssist AI adds value in real workflows
External Teleradiology — Criticals Needing Local Handover
When an external teleradiology provider reports a critical or urgent finding, the local referring team needs to be notified and a follow-up action taken promptly. Without a structured handover process, critical findings can be missed or sit in an inbox unnoticed.
- Critical finding flagged in teleradiology report
- RadAssist AI creates a handover task with urgency level
- Named local owner assigned with due time
- Completion recorded and auditable
Night-time Critical Findings — Pending Follow-up
Urgent or incidental findings reported outside normal working hours often have no immediate clinical owner. They need to be tracked and handed over at the start of the next working day with clear responsibility and an audit trail.
- Out-of-hours report identifies actionable finding
- Task created with "pending handover" status
- Alert surfaced on morning worklist for day team
- Handover confirmed and timestamped
Routine Follow-up Recommendations
Many radiology reports include recommendations for routine repeat imaging in 3, 6 or 12 months. These are low urgency but high volume, and easy to lose track of across departments. Without a system, they often go unbooked.
- Recommendation detected: Repeat CT in 3 months
- Task created with due date calculated automatically
- Placed in shared departmental worklist
- Status tracked from open to completed
Designed to sit alongside existing hospital systems
RadAssist AI adds a dedicated follow-up workflow layer between your existing systems and your governance oversight — without disrupting anything already in place.
Meet the Team Behind RadAssist AI
A multidisciplinary team bringing together healthtech, clinical workflow, artificial intelligence, compliance and IT infrastructure to improve radiology follow-up safety.
Leadership
Ubong Ekeruke
Founder & Chief Executive Officer
Leads vision, strategy and growth of RadAssist AI.
Martins Lever
Business Development & Strategic Partnerships Lead
Supports commercial growth, partnerships and go-to-market activity.
Advisors & Specialists
Vineet Vijay
AI/ML Engineer
MSc Data Science & AI
Focused on extracting structured follow-up actions from clinical report text.
Debbie I.
Regulatory Affairs & Compliance Advisor
IVDR · SaMD · ISO 27001 · DTAC
Expert in IVDR, Software as a Medical Device, AI policy, ISO 27001, NHS DTAC, cybersecurity and digital health compliance.
Rakesh Bhalsod
Clinical Workflow & Imaging Systems Advisor
PACS · RIS · TAG · Radiographer
Senior Specialist Radiographer in PACS, RIS and TAG. Brings real-world radiology workflow, risk and governance insight.
Haider Ali
IT Infrastructure & Cloud Systems Lead
Azure · Microsoft 365 · Entra ID · Intune
Experience in Azure Cloud, Microsoft 365, Entra ID, Intune, IT operations and systems support.
Ready to explore a safer way to manage radiology follow-ups?
Whether you are a radiology leader, digital team, governance lead, or innovation partner, we'd love to show you how RadAssist AI can support safer, more visible follow-up workflows.
No commitment required · UK-based team