1. Request
The process of integrating AI into healthcare imaging begins with identifying an AI tool with potential benefits. FDA approved tools will often provide useful documentation and metrics for decision-making, but FDA approval is not a necessity; beta and home-grown tools are also viable options. The process of identifying an AI tool for clinical use is a joint effort between healthcare professionals and IT specialists, ensuring no single party makes the decision alone. Once there’s a consensus among all stakeholders, a small-scale test can be initiated to evaluate the tool’s effectiveness.
Figure A – A healthcare team reviewing data produced by an AI solution. AI generated art.
2. Test
Next, the ‘Test’ phase kicks off with a small-scale trial to gauge the AI tool’s effectiveness in a controlled environment, where its outputs are compared with existing radiology reports through both quantitative and qualitative analyses. Early testing seeks to answer fundamental questions about the AI’s utility before investing time and money. Does it improve accuracy? Can it process data at the required speed? If initial results are positive, the testing escalates to a broader setting. This larger scale test is where the AI tool is fed more significant data volumes and more complex cases to thoroughly test the tool’s reliability and performance under more demanding clinical conditions.
Figure B – Comparison of an established Calcium Scoring process to an AI tool. This demonstrates an error or miscalculation by the AI tool.
3. Review
In the ‘Review’ phase the outcome of the AI system’s extensive testing is meticulously evaluated, including a detailed report, radiologist feedback, and final recommendations. This decision-making stage assesses whether the AI achieves its goals of improving diagnostic accuracy and speeding up image analysis.
A tool that receives positive validation and radiologist approval for its accuracy and workflow integration may advance to clinical deployment. However, if the AI demonstrates unsatisfactory accuracy that may lead to misdiagnoses or missed diagnoses, it might not be approved for clinical application. Ensuring patient safety and maintaining diagnostic accuracy are of highest importance, with AI solutions required to meet stringent clinical standards before they are adopted into clinical practice.
Figure C – A scatter plot demonstrating a strong positive correlation (96.97%) between the Calcium Scores given by an AI tool and those from established processes.
4. Deploy
With a successful review, the ‘Deploy’ phase commences. Here, legal contracts are finalized, the necessary hardware and software installations take place, and the AI system is integrated into the clinical workflow. Quality metrics are established to evaluate the AI tool’s performance continuously. This phase is about laying the groundwork for the AI tool to become a regular part of the healthcare practice, ensuring it augments rather than disrupts patient care.
Figure D – An IT technician installs new hardware to accommodate the approved AI tool. AI generated art.
5. Monitor
Finally, the ‘Monitor’ phase is an ongoing process where the AI application’s performance is continually observed. The tool is assessed for its accuracy, efficiency, and impact on healthcare outcomes. Any unexpected results are thoroughly investigated and modified accordingly. This phase is crucial for long-term success, as it ensures that the AI system remains a beneficial, functional, and reliable healthcare tool.
Figure E – A computer monitor displaying various charts that track the performance of an AI tool, intended for routine evaluation of the system. AI generated art.
