The ROI of AI in enterprise imaging is a complicated topic, encompassing performance, accuracy, treatment outcomes and financial considerations. In the field of radiology, the rapid development of artificial intelligence has the potential to revolutionize the field by increasing diagnostic precision and improving patient care.
However, the financial aspect of AI adoption is complicated, particularly given the current lack of direct reimbursement for AI applications in medical imaging. Still, AI can indirectly contribute to ROI by increasing the efficiency of imaging providers and supporting roles that enable greater provider productivity and better staffing efficiency, ultimately improving outcomes and reducing the overall cost of healthcare delivery.
Dawn Cram, Principal EI and AI Consultant at The Gordian Knot Groupand his colleague will address this complicated topic at HIMSS25 in March in Las Vegas in a session titled: “Return on Investment in Artificial Intelligence in Enterprise Imaging.”
Cram has over 30 years of healthcare experience in clinical technology, information systems administration, and is a leader in enterprise and departmental imaging systems, clinical information systems, and medical imaging software development.
He has extensive experience in coordinating all phases of systems and application development, strategic and tactical planning, multidisciplinary interoperability, integration and implementation. Its mission to create cost-effective, scalable systems that effectively support physician workflow and application interoperability has helped service organizations and product vendors develop and deploy improved imaging system applications and platforms.
We sat down with Cram to discuss the ROI of AI in enterprise imaging and get a preview of her HIMSS25 session.
Q. Why is the topic of AI and enterprise imaging ROI critical today?
AND. This topic is particularly relevant and timely as the healthcare sector increasingly integrates artificial intelligence technologies to escalate diagnostic accuracy, improve patient care and eliminate tedious workflow steps. In the field of medical imaging, especially radiology, the transformative potential of AI is clear, although financial challenges, such as the lack of direct reimbursement for AI applications, complicate financing.
Still, AI can indirectly escalate ROI by increasing the efficiency of imaging providers, leading to increased productivity, improved staff performance, and lower healthcare costs.
We will offer valuable insights on identifying cost-benefit opportunities and methods for calculating ROI when implementing various AI technologies in enterprise imaging and for various people. Understanding the additional costs of running AI is equally critical and requires consideration to determine the true cost of ownership.
By understanding financial dynamics, healthcare organizations can make informed AI investment decisions that maximize benefits while effectively managing costs.
During the sessions, we aim to provide participants with practical tools, tips and strategies to lend a hand justify AI investments and achieve lasting improvements in imaging operations, even without direct reimbursement. There is a return on investment (ROI) associated with every workflow, process, and implementation of care improvement.
Q. What types of artificial intelligence will you cover during the HIMSS25 session?
AND. We will discuss both clinical artificial intelligence, such as pathology detection algorithms, and process artificial intelligence, such as robotic process automation, in the context of enterprise imaging. By automating routine and repetitive tasks, AI enables physicians to focus on more critical aspects of patient care, thereby improving diagnosis precision and patient outcomes.
Additional cost benefits can be achieved by implementing process AI in supporting roles such as patient scheduling. Artificial intelligence can also be used to streamline imaging processes, reduce the time required for image analysis, and support more effective clinical decision-making.
AI can analyze massive amounts of imaging data correlated with clinical data such as laboratory data and even genomics. It can identify patterns and anomalies that the human eye might miss or would take much longer to evaluate. This could lend a hand detect and treat diseases earlier, ultimately leading to significantly improved patient outcomes and overall reductions in healthcare costs.
While the operate of AI in radiology is widespread, we will also discuss other imaging specialties across the enterprise and the benefits AI can bring to diagnostics and workflows. For example, ophthalmology can operate artificial intelligence to screen and diagnose retinal diseases by implementing algorithms that can analyze fundus images for signs of diabetic retinopathy or macular degeneration.
Dermatology, wound care and other photo-related specialties can operate applications with built-in artificial intelligence to identify the imaged body part, lesion or analyze the size and shape of a wound, as well as provide support for the early detection of skin cancer or potential infections.
Q. What is one takeaway that you see HIMSS25 participants leaving your session with and reporting back home to their organizations?
AND. One of the key takeaways will be the importance of implementing responsible AI, which is crucial to achieving return on investment. Current AI is still inherently stupid and depends on the humans who create it. Before awarding a contract, it is necessary to check how the algorithm was created, trained and tested.
There are many factors to consider when determining whether software companies have developed AI responsibly. This includes whether diverse and representative datasets were used to mitigate bias and ensure equitable patient care that works reliably across demographic groups and regardless of the manufacturer of the data collection device.
A critical aspect in determining responsible clinical AI is compliance with regulatory standards designed to ensure the safety, effectiveness, and reliability of AI algorithms used in diagnostic imaging. Organizations can have greater confidence that an FDA-approved AI algorithm has undergone exacting testing and validation processes, ensuring that algorithms intended to lend a hand physicians analyze images or provide diagnostic insights meet specific quality and safety standards before being deployed in clinical settings.
By complying with these regulations, manufacturers can lend a hand build trust among healthcare providers and patients, ensuring the safety and effectiveness of AI technologies in medical practice.
Some additional considerations when determining responsible AI development include quality management and continuous monitoring capabilities. Clinical AI must be continually evaluated to ensure that algorithms maintain their effectiveness over time, adapting to recent data, clinical scenarios and variances.
This includes ensuring that compliant monitoring mechanisms are in place and implemented to detect and address any issues that arise during AI deployment and over time as imaging and diagnostic technologies evolve.
This session will enable participants to return to their organizations with a better understanding of how to promote and implement AI technologies that are not only pioneering, but also ethical, lucid and offer high standards of patient care.