Digital Imaging & Radiology solutions encompass advanced technologies that help healthcare organizations optimize workflows, care collaboration and enhance clinical decision-making. These solutions reduce diagnostic errors, accelerate turnaround times, and enable more convenient access to imaging services.
Digital imaging & radiology framework
Workflow optimization and intelligence | Solutions that enhance radiologist efficiency and diagnostic accuracy by leveraging artificial intelligence to detect abnormalities, prioritize worklists, provide quantitative insights, and streamline imaging workflows. These platforms optimize resource utilization, reduce wait times, and improve clinical decision-making through intelligent scheduling and automation, ultimately reducing radiologist burnout and improving patient outcomes. Example companies: Aidoc, Viz.ai, Qure.ai, Lunit, Rad AI, Sirona, Synapsica |
Advanced image processing | Solutions that use deep learning algorithms to improve the quality of medical images enabling more accurate diagnosis and interpretation. Features may include the ability to reduce image noise, increase contrast, enhance resolution, and obtain more information from standard scans. Example companies: Subtle Medical, Vista.ai, AIRS Medical, Cleerly |
Point-of-care utilization | Technology that brings advanced imaging capabilities directly to the patient bedside or clinic setting, enabling immediate diagnostic decisions, reducing patient transfers, and facilitating timely interventions through portable technologies. Example companies: Butterfly Network, EchoNous, eXo, ThinkSono |
These are example companies, and not meant to be comprehensive. Did we miss your company? Schedule some time to connect.
The case for digital in imaging & radiology
As healthcare facilities manage increasing volumes of complex studies with constrained resources, AI-enhanced imaging solutions have emerged as essential technologies that transform how diagnoses are made while reducing errors, optimizing operational costs, and improving patient outcomes.
Transform detection capabilities and reduce diagnostic errors
Diagnostic misses and interpretation errors in medical imaging represent a critical challenge for healthcare delivery, with medical diagnoses error rates estimated to be ranging from 10-15%, affecting more than 12M Americans each year.1 AI-enhanced imaging solutions directly address this challenge through computer vision algorithms that can detect subtle abnormalities often missed by the human eye. These systems excel particularly in detecting early-stage cancers, with studies showing AI-assisted mammography increasing early breast cancer detection while simultaneously reducing false positives, in comparison to traditional screening methods.2 By providing consistent analysis of every pixel in every image, AI systems serve as a powerful second reader, helping imaging specialists overcome perceptual and cognitive limitations that contribute to missed findings and diagnostic errors.
Overcome radiologist shortages through intelligent workflow optimization
Radiology departments face an unprecedented capacity crisis, with imaging volumes growing at 5% annually while the radiologist workforce expands by only 2%.3 This widening gap can create dangerous interpretation backlogs, with longer waiting times for non-critical studies. AI-enhanced imaging solutions tackle this challenge by automating routine aspects of image analysis and prioritizing cases based on clinical urgency. This can help reduce interpretation time for studies and improve critical finding notification. By handling routine analyses and flagging abnormal studies, AI systems allow radiologists to practice at the top of their license, focusing their expertise on the most complex cases and consultative roles that drive the greatest clinical value.
Elevate image quality while reducing patient radiation exposure
The diagnostic quality of medical imaging has traditionally been constrained by a fundamental tradeoff between image resolution and patient safety concerns, particularly radiation exposure from CT scans and X-rays, and toxic contrast agents in MRI. Each year, medical imaging contributes approximately 50% of Americans’ total radiation exposure, with an estimated 2% of future cancers attributable to CT scans alone.4 AI-enhanced imaging solutions break this longstanding tradeoff through sophisticated deep learning algorithms that can extract diagnostic information from lower-dose scans. These achievements can be particularly significant for pediatric patients and those requiring repeated imaging for chronic conditions. In addition to safety benefits, AI image enhancement capabilities are extending the clinical utility of existing imaging equipment, with algorithms enabling standard ultrasound devices to identify subtle tissue changes previously visible only with more advanced modalities. By simultaneously improving image quality while reducing patient exposure to harmful radiation and contrast agents, AI-enhanced imaging solutions are fundamentally transforming the risk-benefit calculus of diagnostic imaging procedures.
Sources
- https://www.ncbi.nlm.nih.gov/books/NBK588118/
- https://www.rsna.org/news/2024/june/ai-detects-more-breast-cancers#:~:text=In%20total%2C%2060%2C751%20women%20were,work%20on%20in%20the%20future.%E2%80%9D
- https://www.beckershospitalreview.com/radiology/the-radiologist-shortage-explained.html
- https://www.health.harvard.edu/cancer/radiation-risk-from-medical-imaging, https://www.nbcnews.com/health/health-news/ct-scans-may-much-radiation-researchers-say-rcna195198
