Moving Beyond Efficiency: How Imaging AI Redefines Performance in Healthcare

By Ameena Elahi, Associate Director of AI Initiatives, Penn Medicine

Rethinking the Value of Imaging AI: Focusing Beyond Speed

When health systems start assessing imaging AI, the first question is nearly always some form of: How much quicker will this make us? It’s a fair question. But when AI is evaluated purely through the lens of speed, organizations risk omitting the deeper value proposition. Cognitive load, the mental load needed to process, prioritize, and act on information, should be a core evaluation metric, not because it is a “soft” outcome, but because it directly affects clinician performance, patient safety, and clinician well-being. Ultimately, the value of clinician-facing AI is reflected in what changes for the clinician who no longer has to do everything at once.

Evaluating Imaging AI: What Actually Matters Most

Before contracting a product or piloting AI technology, the priority is determining whether the tool fits the clinical environment. It’s important to understand how you can integrate the technology without disrupting what’s already working.

Most imaging AI tools come with their own portals, dashboards, or separate reading spaces. These artificial intelligence (AI) models are trained on data, and that data has a population, a scanner fleet, and a case mix baked into it. A model trained predominantly on academic medical center imaging may perform very differently in a community radiology setting with older equipment and a different patient demographic.

Some AI functions are designed to help administrators take action based on reporting metrics; some are meant to help the physician do the image reading. These features should be evaluated and solutioned with products that fit the unique need, even if using the same vendor on the same application. Conduct realistic use scenarios with your own radiologists, technologists, administrators, and referring clinicians before procurement decisions are made.

It’s also important to assess if your organization is ready to adopt this technology not just technically, but culturally. By incorporating a change management plan, adoption is more likely to succeed than be delayed or abandoned. Clear communication is needed about what AI will and will not do, along with providing time to adapt workflows to create long-term success.

A clear, confident clinician at the end of a shift is not a soft outcome, but a core system performance metric rooted in cognitive capacity and clarity.

The Quieter Revolution: Reducing Cognitive Load for Radiologists

In terms of the deeper value proposition, cognitive load reduction is where the conversation around imaging AI really needs to evolve. This becomes most visible in day-to-day clinical work. Radiologists engage in a dual workflow exercise: when reading, a sustained, multilayered mental state occurs that keeps telling one which findings are incidental, which are urgent, what needs correlations with earlier imaging, and what needs to be answered urgently. That mental juggling act is draining in a way that metrics, like reads per hour, are not designed to reflect.

Reducing Cognitive Load for Imaging Techs

The same cognitive load is felt by technologists. A CT technologist obtaining a scan isn’t just pressing a button; their tasks include checking the order, selecting the appropriate protocol, adjusting for patient condition, verifying contrast requirements, and anticipating what the radiologist may need beyond the standard protocol. AI can support this process by recommending protocol adjustments based on the indication, patient history, and prior imaging, while also flagging inconsistencies, such as a mismatch between the order and the clinical context. The scan itself may take the same amount of time, but the technologist is spending less mental energy second-guessing decisions and worrying about whether anything was missed.

Setting a New Path Forward: Start with Workflow, Not Technology

Burnout in radiology is real. When organizations evaluate AI solely through a throughput lens, they risk solving the wrong problem. Strategic decision-making should not begin with the technology itself, but with the problem it is meant to address. A common framing question is: What problem are you trying to solve? One way to break that down is to focus first on current capabilities and desired future state, rather than the technology.

Start by mapping where cognitive demand is highest within your imaging workflows. Where do radiologists and technologists most often report being mentally drained? Where do handoffs break down? Where does uncertainty accumulate?

Augmentation Over Automation

Most durable imaging AI implementations are those that keep the radiologist reasonably in the loop, not as a rubber stamp on algorithmic output, but rather as the chief reasoning agent, underpinned by tools that minimize unnecessary mental friction. When piloting, measure more than throughput, survey on decision fatigue, perceived workload, and confidence at the end of the shift, not just volume. Create a business case that includes the cost of burnout, turnover, and the value of incremental efficiency gains.

The clinician who ends a shift feeling competent and lucid, instead of drained, is not a soft outcome, it is a system requirement. AI that protects that state is more valuable than AI that simply works faster.