Table 4.

Overview of integrated AI applications in Southern

Type of AI application	State of adoption/integration	Impact on clinical practice
3D tumour segmentation of vestibular schwannoma on MRI	Ready for prospective validation in research setting	Transition from 2D measurements to automated 3D volume measurement, resulting in time reduction and quality improvement
Normal/abnormal detection chest X-ray	Fully integrated into clinical workflow (validated on prospective data). AI output directly present in worklist of radiologist. Detailed overlay images presented in separated viewer	± 45% of all chest X-ray cases in clinical practice are normal. The algorithm is able to automate reporting for approximately 20% of all normal cases, enhancing clinical efficiency
Lung nodule detection on CT-Thorax	Fully integrated into clinical workflow (validated on prospective data). Output (number of detected lung nodules and percentage of affected lung tissue) presented in PACS worklist and possibility to accept/reject/modify nodule segmentations in PACS viewer	Substantial time reduction in follow-up imaging and improved lung nodule comparisons over time
Bone age measurements on X-ray	Fully integrated into clinical workflow (validated on prospective data). AI report automatically available in PACS	Automated bone age measurements on x-ray, facilitating task differentiation to advanced practitioners
Covid detection and quantification on CT	Fully integrated into clinical workflow (validated on prospective data). AI report automatically available in PACS	Robust quantification of COVID-affected lung parenchyma in all lung segments, significantly enhancing reporting efficiency and quality
Leg angle and distance measurements on X-ray	Fully integrated into clinical workflow (validated on prospective data). AI report is automatically available in PACS. Standardised radiological report based on AI output	Automated leg angle and distance measurements with an AI acceptance rate of approximately 90%
MRI neuro quantification for dementia patients	Fully integrated into clinical workflow (validated on prospective data). AI report automatically available in PACS	Automated quantification of white matter abnormalities and atrophy evaluation
Automatic quality feedback for chest X-ray	Fully integrated in clinical workflow. Automatic quality feedback on iPad after image acquisition	Enhancement of image quality to ensure accurate reporting and prevent the need for patients to return, as low-quality images may otherwise necessitate their return for re-imaging
Large vessel occlusion detection for early stroke detection	Fully integrated into clinical workflow (validated on prospective data)
Fracture detection on X-ray	Implementation phase. Connected to clinical systems and ready for clinical use	Potential impact: decreased reporting time, enhanced diagnostic confidence and subsequently boost job satisfaction, particularly during night and weekend shifts when residents work independently
Scoliosis measurements on X-ray	Implementation phase. Connected to clinical systems and ready for clinical use	Potential impact: automated scoliosis measurements
Automated vertebral fracture assessment on DXA	Development phase, model development and retrospective validation	Potential impact: automated vertebral fracture assessments resulting in significant reduction in reporting time. Prototype has shown positive impact on reader discomfort for annotation
Prostate analysis on MRI	Exploration phase	Potential impact: decreased reporting time by pre-filled structured report based on AI output
AI for tomosynthesis	Exploration phase	Potential impact: decreased reporting time by pre-filled structured report based on AI output
Knee osteoarthritis measurements on X-ray	Exploration phase	Potential impact: automated osteoarthritis measurements. Reduction in reporting time