Hello. I'm Dr Michelle Kittleson, a heart failure transplant cardiologist at Cedars-Sinai in sunny Los Angeles, California. I'm delighted to have this opportunity to present a commentary on artificial intelligence in, including to assess those at risk for heart failure and to catch early signs of illness and improve patient outcomes. as the study of intelligent agents, which can perceive the environment and act as humans do, typically for narrowly defined tasks.
In my opinion, the most exciting of these studies are those that cull data from the electronic health record . It will be incredible to receive an EHR alert advising a screening echocardiogram, or natriuretic peptide assessment for patients who screened as having a high risk for heart failure, especially because the screening tool offers no additional risk to the patient.
Let's move on to determination of prognosis. So much of being a clinician is about predicting the future. Will my patient develop heart failure, and if they develop heart failure, what will their prognosis be? In my opinion, this is an area of great need where we rely mostly on judgment and experience, with variability between clinicians based on their interpretation of many complex data points.
We've discussed AI and the prediction of incident heart failure, diagnosis of heart failure, phenotyping of HFpEF, and determination of prognosis. When can we expect the incorporation of AI technologies into clinical practice? Before that, it's important to note the pitfalls, both scientific and practical.
The third challenge will undoubtedly be adoption and implementation by clinicians. The inertia will probably be due to some mistrust of AI models and"alerts fatigue" due to responding to automated queries in the EHR. Thus, education of the benefits and pitfalls of AI approaches will be essential, as will practical implementation strategies that help rather than hinder clinical workflows.
Short of that, in an ideal world AI would optimize enrollment in clinical trials; forecast the likelihood of adverse events occurring based on patient characteristics, such as age, gender, and medication history; and predict treatment response to individualized therapies. Wegovy to Be Covered by US Medicare for Heart Disease PatientsTargeting Obesity to Improve HFpEF Outcomes
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