The return on investment for artificial intelligence in enterprise imaging is a multifaceted subject that encompasses efficiency, accuracy, patient outcomes and financial considerations. In the realm of radiology, AI’s surge has promised to revolutionize the field by increasing diagnostic precision and improving patient care.
However, the financial aspect of AI adoption is complex, particularly due to the current lack of direct reimbursement for AI applications in medical imaging. Despite this, AI can indirectly contribute to ROI by enhancing the efficiency of imaging providers and supporting roles that offer greater provider productivity and improved staffing efficiencies – ultimately improving outcomes and reducing the overall cost of healthcare delivery.
Dawn Cram, principal consultant, EI and AI, at The Gordian Knot Group, and a colleague will address this complex subject at HIMSS25 in March in Las Vegas in a session titled, “The ROI of AI in Enterprise Imaging.”
Cram has more than 30 years of healthcare experience in clinical technologies, IT systems administration, and leadership in enterprise and departmental imaging systems, clinical information systems and medical imaging software development.
She has extensive experience in orchestrating all phases of system and application development, strategic and tactical planning, multidisciplinary interoperability, integrations, and deployments. Her mission to achieve cost-effective, scalable systems that effectively support clinician workflows and application interoperability has helped guide provider organizations and product vendors to envision and implement enhanced imaging system applications and platforms.
We sat down with Cram to discuss AI ROI in enterprise imaging and get a preview of her HIMSS25 session.
Q. Why is the subject of AI ROI and enterprise imaging an important one today?
A. This topic is especially relevant and timely as the healthcare sector increasingly integrates AI technologies to enhance diagnostic accuracy, improve patient care and eliminate tedious workflow steps. In the realm of medical imaging, particularly radiology, AI’s transformative potential is clear, though financial challenges, such as the lack of direct reimbursement for AI applications, complicate funding.
Despite this, AI can indirectly enhance ROI by boosting the efficiency of imaging providers, leading to increased productivity, better staffing efficiencies and lower healthcare costs.
We will offer valuable insights into identifying cost-benefit opportunities and methods for calculating ROI when implementing various AI technologies in enterprise imaging and for differing personas. Understanding the ancillary costs of running AI is equally important and requires consideration to determine true cost of ownership.
By understanding the financial dynamics, healthcare organizations can make informed decisions about AI investments, which maximize benefits while effectively managing costs.
During the session we aim to provide participants with practical tools, tips and strategies to help justify AI investments and achieve sustainable improvements in their imaging operations, even without direct reimbursement. Each workflow, process and delivery of care improvement has an associated ROI.
Q. What kinds of AI will you be addressing in your HIMSS25 session?
A. We will be discussing both clinical artificial intelligence such as pathology detection algorithms and process AI 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 diagnostic precision and patient outcomes.
Additional cost-benefits can be achieved when deploying process AI for support roles, such as patient scheduling. AI also can be used to streamline imaging workflows, reducing the time required for image analysis, and supporting more efficient clinical decision making.
AI can analyze vast amounts of imaging data correlated with clinical data such as labs or even genomics. It can identify patterns and anomalies that might be missed by human eyes or would take significantly longer to assess. This can help achieve earlier detection and treatment of diseases, ultimately leading to significantly improved patient outcomes and reduced healthcare costs overall.
Although AI’s application in radiology is prevalent, we also will discuss other imaging specialties throughout the enterprise and how AI can benefit their diagnostics and workflows. For example, ophthalmology may use AI in the screening and diagnosis of retinal diseases, deploying algorithms that can analyze fundus images for signs of diabetic retinopathy or macular degeneration.
Dermatology, wound care and other photo-production specialties may use apps with embedded AI to identify the body part being imaged, lesion, or wound size and shape analysis, and provide support in the early detection of skin cancer or potential infections.
Q. What is one takeaway you see HIMSS25 attendees leaving your session with and applying when they return home to their organizations?
A. One key takeaway will be the importance of deploying responsible AI, which is critical to capturing any ROI. AI today is still inherently dumb and relies upon the humans creating it. Validating how an algorithm has been created, trained and tested is necessary prior to procurement.
There are many factors to consider in determining if the AI was developed responsibly by software manufacturers. This includes whether diverse and representative data sets were used to mitigate bias and ensure equitable patient care that performs reliably across different demographic groups and regardless of acquisition device manufacturer.
A critical aspect in determining responsible clinical AI is compliance with regulatory standards designed to ensure the safety, efficacy and reliability of AI algorithms used in diagnostic imaging. Organizations can better trust that an FDA-cleared AI algorithm has undergone rigorous testing and validation processes, ensuring those meant to aid physicians in analyzing images or provide diagnostic insights meet certain quality and safety standards before being deployed in the clinical setting.
By adhering to these regulations, manufacturers can help build trust among healthcare providers and patients, ensuring AI technologies are safe and effective for use in medical practice.
Some additional considerations in determining responsible AI development include quality management and ongoing monitoring capabilities. Clinical AI should be continuously evaluated to ensure the algorithms maintain their performance over time, adapting to new data, clinical scenarios and variances.
This involves ensuring compatible monitoring mechanisms exist and are implemented to detect and address any issues that arise during the AI’s deployment and over time as imaging technologies and diagnostics evolve.
This session will enable attendees to return to their organizations with a better understanding of how to advocate for and implement AI technologies that are not only innovative but also ethical, transparent and offering high standards of patient care.
Cram’s education session, “The ROI of AI in Enterprise Imaging,” is scheduled for Tuesday March 4, at 2 p.m. at HIMSS25 in Las Vegas.
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