Picture archiving and communication systems (PACS) have served as the backbone of medical imaging workflows for decades.
But AI is now poised to take PACS capabilities to the next level. In this article, we’ll explore emerging opportunities to incorporate AI within PACS medical imaging environments to automate repetitive tasks, speed diagnoses, and improve clinician satisfaction.
AI Can Unlock Next-Level PACS Performance
While traditional PACS platforms focused solely on storing and transmitting studies, integrated AI unlocks more advanced clinical functionality:
- Auto-labeling scans by anatomy speeds indexing and search.
- Protocol recommendation engines suggest optimal sequences.
- Automated measurements reduce mundane workflow steps.
With its ability to continually analyze vast imaging datasets and provide insights beyond human capacity, AI can elevate PACS from passive archives to active assistants.
Moving Beyond Task-Focused AI Add-Ons
Initial forays into PACS-enabled AI have centered on deploying standalone algorithms for niche tasks like lesion flagging. But modern AI integration takes a more holistic approach:
- Multi-task frameworks speed diverse workflow steps together.
- Tight integration within PACS replaces after-the-fact add-ons.
- Cloud-based AI enhances performance without on-premise servers.
This expanded scope and seamless embedding of AI aims to make workflows smoother rather than introduce more piecemeal systems.
AI Can Triage Studies and Flag Abnormalities
One major pain point in radiology is managing the exploding volume of studies, especially for time-sensitive diagnoses like strokes. AI triaging applications can help by:
- Segmenting anatomy automatically to orient radiologists.
- Identifying trauma/hemorrhage characteristic for rapid assessment.
- Flagging incidental findings for expanded review.
- Rating study quality and technical adequacy.
This speeds up the process of prioritizing and distributing studies for reading by highlighting the most urgent or unusual cases.
Go Beyond Binary Normal/Abnormal Findings
While AI can reliably detect anomalies, additional context is equally important. Advanced systems provide richer insights like:
- Anatomical localization of flagged findings.
- Measurements of lesions and temporal changes.
- Comparisons of the case with massive imaging datasets.
- Citations of relevant literature and guidelines.
This supplementary content helps radiologists understand the clinical relevance of AI flags for more informed diagnoses.
Automate Repetitive Post-Processing Tasks
Many PACS workflows involve laborious post-processing steps like segmentation to extract quantitative data. AI automation can take over these tiresome tasks:
- Auto-segmentation of organs, tumors, and other structures of interest.
- Volumetric calculations without manual measurements.
- Bone suppression for improved lung nodule detection.
- Denoising to enhance visual clarity.
Alleviating the burden of manual post-processing lets radiologists stay focused on higher-level clinical interpretation.
Maintain Ultimate Human Oversight
While AI can hugely assist with tasks like segmentation, radiologists must still review automated results. Strategies like the following maintain safety and quality:
- Present both original and processed images for comparison.
- Enable manual override of any AI-generated contours or masks.
- Provide aggregate performance metrics for algorithms to identify failures.
- Support asynchronous clinician feedback to continually improve algorithms.
Though powerful, AI remains a tool to augment radiologists who must bear responsibility for all clinical conclusions.
AI Can Aid Comparisons Across Timepoints
A key part of radiology is tracking changes between serial imaging studies over time. For tasks like this, AI delivers capabilities well beyond human cognition alone:
- Precisely register multi-year series of images.
- Quantify even subtle anatomical changes unnoticed by the eye.
- Incorporate thousands of prior cases to contextualize observations.
- Maintain perfect recall of all historic imaging findings.
AI amplifies understanding of disease progression and response to therapies based on robust longitudinal analytics.
Surface Relevant Archival Images for Context
Radiologists ordering new imaging studies may lack ready access to relevant prior images for comparison. AI can automatically surface highly pertinent past cases from archives:
- Retrieve comparison studies for the same patient and anatomy.
- Identify cases with similar, highly unusual findings.
- Filter selections are based on factors like demographics and diagnoses.
- Present side-by-side matched view layouts.
This relieves radiologists from combing through mountains of images to find analogs that offer clinical perspective.
The synergistic combination of AI and PACS can transform efficiency and diagnostic accuracy as radiology practices increasingly embrace intelligent automation.
With thoughtful integration guided by clinical needs, AI-enabled PACS can get the right imaging insights to the right people at the right time.
