Article

Revolutionizing medical imaging workflow with AI and Automation Science

Anita Mangtani
Sr. Healthcare Consultant

Tanvi Gupta
Healthcare Consultant

Apr - 22

Insights

Medical imaging (including cellular and molecular imaging) plays a crucial role in Biomedical Research, providing physicians and researchers with essential insights at speed to understand the basis of health, biological pathways and the pathophysiology of diseased states. Also with the rapid increase in medical imaging endpoints, especially for Oncology trials, precision and accuracy is becoming even more important for evidence generation for decision making. However, the traditional medical imaging workflow can be complex and time-consuming, involving multiple steps from image acquisition, management to interpretation.

Current medical imaging practices

Currently, medical imaging involves capturing images using sophisticated imaging equipment operated by trained technicians. These images are then interpreted by radiologists, who provide diagnostic reports to the referring physicians. The process often includes multiple steps: patient preparation, image acquisition using consistent image acquisition protocols, image processing, and interpretation.

Despite advancements in imaging technology, the workflow remains largely manual and time-consuming, requiring significant human intervention at each stage.

Challenges with the current set up

Traditional imaging workflows in diagnosis and clinical trials lack consistency and well-organized data management practices that can impact efficiency, data integrity & regulatory compliance issues. This can lead to delays in detection, diagnosis, treatment start and long term monitoring of diseases, affecting patient outcomes and quality of life.

High workload and burnout: Pathologists & Radiologists are often overwhelmed by the sheer volume of images they need to review, leading to burnout and increased risk of diagnostic errors.
Variability in interpretation: Human interpretation of medical images can be subjective, leading to variability in diagnoses and treatment plans.
Need for seamless data Interoperability across various imaging modalities and platforms and ensuring combination of imaging data with other data modalities
Image data quality issues for accurate diagnosis- AI is constrained by a lack of high quality, high volume, longitudinal, outcomes data
Delays in diagnosis: The manual nature of the workflow can result in delays, impacting patient outcomes, especially in critical cases.
Resource constraints: Many healthcare facilities face shortages of trained radiologists and imaging technicians, further straining the system

How AI and automation science address these challenges

Artificial intelligence (AI) and automation science have the potential to revolutionize medical imaging by streamlining workflows, faster pre-processing, improving analysis efficiency, and enhancing diagnostic accuracy. With an industry focus on early disease detection, advanced diagnostics and personalized medicine, Healthcare & Life sciences organizations are integrating AI based solutions across practices, diagnostics solutions and patient care. In fact the FDA has now cleared 700 AI healthcare algorithms, more than 76% in radiology.1 In environments lacking access to imaging specialists, AI expedites definitive diagnoses and reduces time to critical care.

I. AI-enabled workflow automation

AI plays a pivotal role in streamlining medical imaging workflows, allowing pathologists & radiologists to focus on critical tasks. Here’s how:

Automated scheduling: AI-powered scheduling systems intelligently allocate appointments based on patient needs and resource availability. This efficiency ensures optimal utilization of imaging facilities.
Image routing: AI algorithms analyze images and route them to the most suitable radiologists. Whether it’s a musculoskeletal specialist or a neuroradiologist, AI ensures efficient distribution of workload.
Report generation: AI can generate preliminary reports by extracting relevant findings from images. Radiologists can then review and validate these reports, saving time and reducing administrative burden.

With this blend of human expertise and AI, pathologist & radiologists can focus on more complex cases, diagnosis, and patient care. This also enables workflow efficiency & greater collaboration between clinicians and pathologists’ across sites.

II. Using imaging to curate research data

In the realm of scientific research, assembling relevant data cohorts can be akin to navigating a dense forest. Researchers grapple with vast datasets, seeking meaningful patterns. Here, imaging technology emerges as a powerful compass.

AI-driven exploration:

Modern platforms leverage AI and natural language processing to swiftly scan through extensive image datasets.
Imagine you need to create a cohort based on lesion size, tumor characteristics, or organ specifics. AI algorithms zero in, extracting pertinent data from the image repository.

Amazon OpenSearch Service:
- Among these tools, Amazon OpenSearch Service stands out. It enables efficient searching and analysis of unstructured data.

Researchers can uncover hidden insights, bridging gaps in their knowledge.

Conclusion

CitiusTech recognizes the complexity of building an enterprise-wide medical imaging infrastructure. While there is awareness of the potential of AI, we feel the true benefits remain largely untapped. Our mission is to collaborate with our customers and the life sciences industry to advance imaging for better treatment outcomes and help advance precision medicine. Towards this, help them adopt the true transformative potential of AI-ML platforms, streamline diagnostics workflows, prepare/process medical imaging & other data sources for AI algorithm enhancement/training & big data management, all of this while ensuring robust security, privacy, and performance to support a future of unparalleled insights.