Medical imaging
Connect worklists, diagnostic viewers and AI findings so radiologists can act in context. Prioritization and detection need transparent review, correction and follow-up.
RESPONSIBLE AI FOR HEALTHCARE
Intelligence
with purpose.
Clinical work is complex. AI should make information easier to use, give care teams more time for patients and earn trust through evidence and oversight.
Explore our focus ↗
WHERE AI CAN HELP
From the image to the clinical encounter.
Explore how imaging platforms, AI assisted documentation and connected clinical records can support the people making care decisions.
Documentation
Turn consultations into editable draft notes, referral letters and coding suggestions. The clinician reviews and approves what enters the health record.
Connected data
A vendor-neutral imaging archive, EHR integration and consistent metadata make information accessible across specialties without losing provenance or access controls.
OUR APPROACH
Trust is an operational discipline.
The right question is not simply whether an algorithm performs well in a test. It is whether the full workflow improves care in practice, for the patients and professionals who use it.
01
Start with a clinical problem
Define intended use, users, setting and the decision the tool may influence.
02
Evaluate in context
Measure quality, subgroup performance, usability and the effect on care teams.
03
Keep people in control
Design review, escalation, monitoring and clear ownership throughout deployment.
CONNECTED CARE WORKFLOWS
Good AI does not live in a separate tab.
Its output belongs in the clinical workflow, with the right context, the right handoff and a human decision at the end.
One diagnostic view
Bring radiology, pathology and other clinical images together. Connect worklists, viewers and AI outputs so specialists can compare evidence across encounters.
Less time on the record
Draft visit notes, letters and coding suggestions from the consultation, then review and transfer them into the EHR. Track both time saved and correction effort.
Evidence after launch
Follow performance by setting and patient group. Record model versions, monitor incidents and give clinicians a clear route to challenge an output.
Our editorial framework draws on published guidance for clinical AI, imaging interoperability and human oversight. Read the WHO ethics and governance guidance and the Radiology review of workflow integration standards.
THE JOURNAL
Ideas for responsible deployment.
Analysis of healthcare AI use cases, implementation choices and the evidence behind them.
MEDICAL IMAGING · 26 SEPTEMBER 2026
Making imaging AI useful at the point of reading
Why workflow integration, clear review and evaluation after rollout matter as much as algorithm accuracy.
Read article ↗Ambient AI and the clinical note
A practical look at draft notes, verification and meaningful measures of time saved.
Read article ↗A checklist for evaluating healthcare AI
From intended use and data quality to monitoring and accountability in production.
Read article ↗Enterprise imaging as an AI foundation
Why archives, workflow and multiple specialties should be considered together.
Read article ↗From consultation to approved record
A closer look at notes, codes, referral letters and EHR handoff.
Read article ↗RAI
Care deserves AI
that earns its place.
Responsible AI for Healthcare.