Enthusiasm around AI in healthcare often dims when day-to-day challenges arise. Poor workflow integration, alert fatigue, lack of transparency and fragmented interfaces can frustrate users, complicating adoption and undermining the technology’s potential. To overcome these obstacles, healthcare organizations must reimagine how AI operates – not as isolated tools but as a cohesive, interconnected system.
This is where orchestration becomes critical. Acting as an automated “conductor,” orchestration ensures the right AI algorithms are applied to the right imaging scans at the right time. Coupled with a platform-based approach that provides unified interfaces, seamless data management and robust integrations, orchestration empowers health systems to maximize the power of AI and embrace transformation.
Orchestration refers to the automated deployment and management of AI algorithms across imaging studies. More specifically, this action is “agentic orchestration” — an AI model’s ability to continuously process heterogeneous data from its environment, normalize that data and observe the normalized output.
With agentic orchestration, when specific parameters or data characteristics emerge, the AI triggers additional solutions to take action on behalf of the human end user. This means:
However, not all AI solutions offer true, agentic orchestration capabilities. Today’s conventional approach relies on manual workflows deployed and managed at the individual scanner level, while also accounting for institutional data heterogeneity and its constant evolution. It’s like trying to hit a moving target while wearing opaque glasses – you might hit what you can see, but you’ll inevitably miss what you cannot see.
With image-based orchestration, like Aidoc’s aiOS™, scans are analyzed using both text and computer imaging. The AI is always on, meaning all relevant algorithms are run on all anatomy present — not just one algorithm running against the initial dedicated pathology.
The next evolution in clinical AI requires more than siloed algorithms to operate effectively – it needs an always on solution that adapts in real-time to ensure all relevant data is continually analyzed without disruption.
1. Maximizing Algorithmic Yield
Yield isn’t just about running multiple algorithms on many scans; it’s about maximizing the potential of each algorithm by applying it to every relevant study – whether dedicated or incidental – based on anatomy and imaging parameters. This approach captures every possible insight, to help ensure nothing is overlooked.
2. Optimizing Algorithmic Performance
Orchestration ensures algorithms receive the most relevant and high-quality data to analyze by selecting the best parts of the unfiltered data sent from the modality (i.e. CT, MRI, etc.), based on the specific use case, such as stroke or pulmonary embolism. It identifies all potential studies, selects the most relevant series within them and balances thoroughness with efficiency. This built in-capability helps ensure accuracy and speed, without compromising either.
3. Improved Awareness
The combination of maximizing algorithmic yield and performance enables AI to analyze all visible anatomy – even partial anatomy – enabling physicians to uncover incidental findings that expand diagnostic reach and improve patient outcomes.
In a 32-month study at Jefferson Einstein Healthcare, Aidoc’s AI orchestration outperformed traditional metadata-based methods. Aidoc-enabled radiologists identified 1.8% more pulmonary embolism scans and 7.0% more intracranial hemorrhage scans, capturing over 6,000 additional patients and 600+ positive cases.1
Orchestration represents a significant shift in how AI supports the delivery of healthcare, offering a unified approach to accuracy, scalability and adaptability. Want to learn more? Schedule a meeting with an Aidoc AI expert to discuss your facility’s specific challenges and opportunities.
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