Two late-breaking studies presented at ESC Congress 2024 in London looked at the use of artificial intelligence (AI) to aid in the identification of patients undergoing a stress echocardiogram who could benefit from further treatment, and to help with identifying and managing myocardial infarction (MI) in patients presenting to the emergency department (ED).

In the PROTEUS trial out of the UK, researchers observed no significant differences in the appropriateness of standard clinical decision-making vs. AI-augmented decision-making when selecting patients for invasive coronary angiograms. Nor were there major differences in the rate of related acute coronary events within six months. However, researchers said that AI-augmentation did improve decision-making for less-experienced clinicians.

"It is well reported that clinician performance in interpreting stress echocardiograms ranges widely according to the experience level of the operator," said lead author Ross Upton, MD. "The results of this trial suggest that AI has the potential to bring all operators, regardless of experience, up to the same level of accuracy." Additionally, he noted that, "while the PROTEUS trial did not demonstrate meaningful differences in all-comers, the AI diagnostic may also benefit specific subgroups of patients in whom decision-making is known to be more complex."

In the RAPIDxAI trial, the use of AI to help identify and manage MI in patients presenting to the ED with suspected cardiac conditions did not improve cardiovascular outcomes. However, researchers said it was safe and increased the adoption of evidence-based care.

Specifically, they noted that patients who were not classified as type 1 MI by AI-driven decision support were less likely to undergo invasive coronary angiography compared to those in the usual care group. The patients classified as having a type 1 MI by the AI-based decision support were more likely to be prescribed statins (82% vs. 68%, respectively); more likely to be given antiplatelet therapy (56% vs. 44%); and more likely to be prescribed a mineralocorticoid inhibitor (56% vs. 44%).

"A key promise of AI in health care is as a tool to help medical professionals diagnose patients faster and more accurately as well as more objectively quantify prognosis, ultimately allowing them to initiate the appropriate treatment sooner to optimize patient outcomes," said lead author Kristina Lambrakis, MD. "Our [trial] did not improve clinical outcomes; however, it did highlight the ability of real-time AI to influence clinical decisions and practice towards evidence-based care. Greater adoption of AI insights and integration of AI insights within clinical workflows will likely be required to improve clinical outcomes."