Emerging PACS-related technologies are redefining the horizon of diagnostic medical imaging and providing healthcare organizations exciting possibilities of transforming patient care. Dive in with us as we discuss the following key trends revolutionizing the medical imaging landscape while laying the foundation of a new chapter in the history of radiology and the future of PACS:
Artificial Intelligence (AI) Usage in Radiology and Medical Imaging
Radiologists are no strangers to computer-aided detection and diagnosis (CAD) systems, thanks to the inception of mammography and chest x-ray applications in the 1960’s. Between progressive movements in algorithm development and increased accessibility of computational resourcing, integrated AI can elevate the radiological decision process to a greater, more serviceable standard.
To begin, what is artificial intelligence (AI)? AI is generally used to describe cognitive functioning (e.g., problem solving, learning, etc.) as imitated by a technological device or entity. Also in reference to the computer science discipline, AI encompasses the development of systems geared towards carrying out work normally undertaken by human intelligence.
Two specific techniques that assist in the cultivation of AI are machine learning (ML) and deep learning (DL). Widely used in medical imaging (particularly enterprise imaging), machine learning comprises all methods that permit computers to learn from data without requiring direct programming. Deep learning, on the other hand, is an emerging technique associated with ML and inhabits the broad AI umbrella. These representation-learning centered methods involve various representational layers through which raw data is decoded for detection or classification tasks to be executed.
As trending in human-grade artificial intelligence development grows, the innovation opportunities for fields like medical imaging and radiology are imminent. Beyond the predicted initial scope of automated tasking (e.g., language translation, surgery execution…even book writing!), AI when integrated into a PACS system may offer the potential of enhancing a radiologist’s work in general by eradicating tedious, time-consuming repetition.
However, the greatest hurdle of AI-integrated PACS solutions in radiology at present lies with artificial intelligence algorithms. Current available algorithms provide a limited ability to operate successfully with the monolithic nature of many in-market PACS systems. Many AI vendors may be inspired to engineer custom PACS interfaces, yet experts surmise a thorough rethinking (where a PACS is constructed around a complete AI framework) is what is required to assure successful widespread adoption of artificial intelligence in radiology.
Medical imaging supported by artificial intelligence offers invaluable potential in radiology and radiological interpretation through improved accuracy and optimized productivity. Some prime use cases that may best exemplify this value include:
The benefits of artificial intelligence-integrated radiology and medical imaging are projected to be far-reaching, greatly influencing the future of PACS systems:
- More efficient workloads with the reduction of laborious tasks (e.g., structure segmentation)
- Decreased mis-reads or “misses” by fatigued or distracted radiologists thanks to acute anomaly (e.g., subtle lesions) detection
- Enhanced diagnoses by way of more in-depth image scanning (identification of items beyond human perception – for example, molecular markers within tumors)
- Better data organization and stewardship
These prospective benefits from AI-powered PACS solutions of the future are certain to lead the way towards better quantitative imaging and improved patient outcomes.
Though limited AI functionality is available in a PACS system, many next-generation VNA’s are closing that particular gap with the incorporation of AI into their design.
Radiology Workflow Automation in Healthcare
The automation of radiology workflows has been fundamental in the pervasive provision of premium optimized healthcare. Designed to leverage technology towards enhancing the delivery of radiology, the automation available within many in-market PACS and RIS systems have contributed immensely to better patient care and reduced turnaround times resulting directly from improved workflows (i.e., the elimination of manual tasks), visualization, and collaboration capabilities.
The seamless interoperability, data centralization, and dynamic scalability characteristic of VNA’s further expands the possibilities for radiology workflow automation equaling in increased time-savings for clinicians and patients.
As this trend in radiology workflow automation continues to grow – particularly in the face of industry drivers such as the movement towards value-based reimbursement (VBR) models and the ensuing need for IT modernization, up-and-coming VNA systems offer the next step in metamorphosing medical imaging and radiology.
Modern VNA’s utilizing artificial intelligence machine learning technology will have the ability to transform the traditionally tedious work (e.g., preliminary diagnosis, annotations, report creation, etc.) into effortless execution. Acting as radiology “smart assistants”, these advanced VNA systems of the future will be essential in reducing radiologists’ workloads while freeing their time to address other crucial patient care elements (e.g., image consultations and data extractions). Additionally, AI-supported VNA’s will champion better patient care by boosting the frequency of early detections thereby minimizing misdiagnoses.
Radiology Workflow Orchestration
Workflow orchestration is a quintessential touchstone of enterprise imaging. Nowadays, healthcare enterprise systems typically consist of various PACS solutions from disparate vendors across widespread networks of imaging centers and hospitals.
To safeguard premium value-based healthcare, successfully implemented workflow orchestration requires a balance in facilitating key factors such as efficiency, SLA compliance, radiologist and subspecialist availability…all the while with an eye on alignment of departmental objectives.
Furthermore, workflow orchestration supports adept workload management while ensuring timely, appropriate assignment of problematic cases by enabling fair case distribution betwixt qualified imaging professionals colocated across healthcare enterprises.
VNAs can serve as a quantum leap forward for healthcare enterprises seeking to realize the future of diagnostic medical imaging. These same organizations desiring to avoid venturing a pricey PACS system replacement can instead consider the more cost-effective workflow consolidation afforded by VNA solutions.
Through comprehensive radiology workflow orchestration, healthcare enterprises are on track to actualizing speedier treatment and turnaround times resulting in improved patient care as well as clinician satisfaction.
Web-Based Radiology Systems
Many healthcare organizations and medical imaging practices are increasingly transitioning their existing on-premises PACS systems to cloud PACS solutions. Immediate advantages of utilizing these platforms include cost effectiveness, scalability, and perhaps most importantly, data security.
Cloud-based PACS solutions can sufficiently support a broad range of medical imaging environments, from small-to-midsized practices to larger-scale facilities. However, medical imaging enterprises can benefit better in leveraging the advanced data management capabilities of a vendor neutral archive (VNA).
RIS/PACS Combination Systems
RIS/PACS software solutions will continue to bolster positive impacts in healthcare of the future. The typical advantages these radiology PACS systems offer healthcare providers – improved workflows, seamless integration, reliable data security, flexible storage scalability – are of perpetual value in optimal business uptime and overall cost and time savings.
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