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.
From the consultation to the diagnostic image, RAI helps care teams document, find and use clinical information with human oversight at every step.
Explore solutions ↗
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
Capture the encounter, prepare structured notes and follow-up documents, suggest codes and support clinical review. The clinician approves the record.
Connected data
Connect clinical records, diagnostic images and approved AI output across care settings with clear provenance and 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.
RAI SOLUTIONS
One clinical workflow, from conversation to record.
Clinical teams can use AI where the work happens: during the encounter, while reviewing evidence and before information reaches the patient record.
Ambient notes and documents
Prepare structured, editable notes from consultations, along with referrals, patient letters and follow-up summaries. Tailor templates to a specialty and review each draft before use.
Codes in context
Surface relevant diagnosis and procedure code suggestions alongside the note. Let clinicians verify, search and select the codes that belong in the record.
Evidence at the point of care
Bring relevant clinical guidance and patient context into a reviewable workflow. Present sources and uncertainty clearly so professionals can make the final decision.
Voice into structured work
Dictate and revise notes or documents when a live consultation capture is not appropriate. Support hands-free drafting while preserving an editable review step.
Approved EHR handoff
Integrate with electronic health records and hospital systems so approved notes, documents and codes move into the right chart with provenance and controls.
Images and AI together
Connect radiology, pathology, worklists, viewers and vendor-neutral archives. Show AI findings beside the source images for specialist review.
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 ↗Before AI reads a slide: validation in digital pathology
A practical look at whole-slide imaging, external validation and human review before pathology AI enters routine care.
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.