Remote monitoring, AI to play key roles in the future of cardiology
Remote monitoring offers a new era of patient care
Remote patient monitoring has long been a staple in managing cardiac health, but recent innovations are pushing the boundaries of what is possible. Maddox discussed the potential of ambient sensing technology, which involves biosensors placed in the home environment. These sensors, such as sensors under a mattress to capture ballistic cardiographs, can continuously monitor heart rate, breathing rate, and other biometrics without any patient intervention. This passive data collection provides nearly 100% fidelity, offering a reliable stream of information that can predict heart failure exacerbations and other cardiac issues.
“The more we can make it automatic and happening in the background, the better off we’ll be in terms of data collection and patient health insights,” Maddox explained.
This shift from active to passive monitoring represents a significant leap in patient care, reducing the burden on patients and improving the quality of data collected. Other sources of cardiac data being investigated include ECG and other biometric monitoring from sensors in toilet seats, car steering wheels and mobile phones.
But by making data easier to record it is also creating new challenges for clinicians.
“It’s a tidal wave of data and we’re already overwhelmed with the amount of data we have to manage every day. We’re starting to see really helpful improvements in data management from AI,” Maddox explained.
AI in cardiology is revolutionizing data management and imaging
The U.S. Food and Drug Administration has cleared nearly 900 clinical AI algorithms, and cardiology has the second highest number of AI approvals behind radiology. Many of these are concentrated in ECG and image analysis.
Maddox said the greater influx of patient data from remote monitoring and other sources presents a challenge in data management. AI is now being used to help track data from remote monitors and to record events, sending an alerts to clinicians when preset thresholds are passed. AI is also being used to look at the ECG data and identify atrial fibrillation and other arrhythmia, and look for signs of worsening heart failure.
Generative AI also is emerging as a powerful tool to help cardiologists spend more time with patients and less time entering data into the electronic medical record (EMR). AI models such as ChatGPT are now commercialized to automate clinical documentation, reducing the administrative burden on clinicians. Maddox said generative AI can record an entire office visit conversation and automatically create a condensed report and pull data to fill in various discreet data fields. He said this allows 80% or more of a report to be completed without physician intervention.
AI opens opportunities for opportunistic cardiology exams
Opportunistic screenings for diseases can be enabled by autonomous algorithms working in the background of clinical systems that can review imaging exams, ECGs and data from patient records to look for signs of various clinical problems. The AI can spot issues early on before they become symptomatic and the opportunistic review by the AI offers a sort of two-for-one deal on tests that are unrelated to the reasons why a test was ordered.
On a large scale, Maddox thinks AI has the potential to identify at-risk patients earlier and with greater accuracy. The use of passive AI screenings of patients outside of clinical settings, using smartphones, toilet seat and bed sensors, may also detect issues and notify the doctor. This is widely expected to help initiate earlier interventions before a disease progresses to being symptomatic and when it is much easier to treat a patient.
The future of preventive cardiology
Maddox said AI is also helping automate many aspects of cardiac imaging to make exams more accurate and reproducible.
One of the most promising areas of AI in cardiac imaging is in soft plaque analysis. AI can automatically and reproducibly assess plaque characteristics in CT scans, which could be a game-changer in preventive cardiology.
“AI-assisted imaging might reveal new plaque characteristics that we need to address to prevent myocardial infarctions,” Maddox suggested.
Cardiac imaging experts at recent Society of Cardiovascular CT (SCCT) meetings predict the use of AI to detect coronary inflammation and the composition of soft plaques will be a game-changer for preventive cardiology in the next few years.
“We need rigorous evidence to ensure these technologies provide high-quality, additive information. The challenge will be integrating these tools into clinical workflows to maximize their value,” Maddox explained. “We also need to demand high standards of evidence and adapt our workflows to fully realize the benefits of these technologies.”
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