Your heart is one of the most important muscles in your body, pumping oxygen and nutrient-rich blood so that everything can function as it should. Sometimes, the heart gets weaker, and this can make it hard for the organ to keep up with the needs of the body. When this happens, it can lead to a condition known as congestive heart failure. Emerging research shows that AI and machine learning could be the perfect pair to understand the progression and even to detect subtle changes in how the heart functions. Let’s take a closer look at how they are using these technologies.
Understanding Heart Failure
Globally, cardiovascular diseases are considered the number one cause of death. There are several conditions that can affect the cardiovascular system, with heart failure being a particularly dangerous one.
Research already shows that there has been an increase in the cases of heart failure when comparing data from 1990 up until 2019 [1]. A research paper published in the Journal of Cardiac Failure also found that an estimated 6.7 million individuals in the United States have heart failure. They estimate that by 2050, this number will increase to 11.4 million [2].
Heart failure is a serious disease of the cardiovascular system where the heart cannot pump blood in a way to satisfy the needs of the body. This causes some parts of the body to be deprived not only of blood but also of essential nutrients, as well as oxygen.
People with heart failure may notice symptoms like trouble breathing and fatigue. It is also common to experience swelling that affects the ankles and legs with heart failure.
Something important to note is that heart failure is generally not considered a disease on its own. Instead, it is usually a consequence of other cardiovascular conditions that affect the heart muscle.
Heart failure is also chronic, which means it persists long-term. Over time, the condition tends to progress, especially when underlying factors are not addressed with the appropriate treatment.
Conventional Monitoring and Treatment Approaches
Heart failure is diagnosed following a number of procedures. It starts with a provider understanding the patient’s symptoms, followed by a physical exam. A provider also has to do a couple of tests to determine if their suspicion of heart failure is correct. These tests generally include blood tests, X-rays of the chest, and echocardiograms.
When heart failure is diagnosed, treatment will then focus on helping to reduce the symptoms a patient experiences. The provider also has to understand what is causing the heart failure, which can sometimes lead to the diagnosis of cardiovascular disease.
Constant monitoring is important for heart failure, since it’s a chronic condition that worsens over time [3]. In addition to monitoring the heart muscle, healthcare providers also need to keep track of other conditions that might be affecting the heart and its ability to pump blood effectively through the body.
This generally involves having the patient come back for tests on a regular basis. If progression of heart failure is found, the healthcare provider will then make changes to the patient’s treatment plan. Their treatment plan may include medication and lifestyle changes. In some cases, heart failure also requires the implantation of certain devices or a surgical procedure to help the heart pump blood.
While regular tests can help doctors determine when heart failure progresses, the current monitoring techniques lack efficiency. This is mostly due to the fact that subtle changes are not detected during these tests. Providers rather look for more noticeable changes in heart function when comparing new tests to older ones.
How AI is Changing the Scope of Heart Failure Monitoring
Artificial intelligence has already proven very helpful in the healthcare sector. Now, researchers are also turning to AI, along with machine learning models, to help improve the monitoring of heart failure patients.
One interesting find comes from the University of Leeds, where researchers were able to develop an AI tool to assist with the early detection of heart failure [4]. Detecting this condition early on can have a significant impact on the prognosis. It helps to ensure treatment is provided before it becomes a serious threat to a patient’s health.
Dr. Ramesh Nadarajah from the School of Medicine explained that they have combined the potential of artificial intelligence with machine learning tools. Their goal is to prevent admissions to a hospital by providing patients with the ability to identify heart failure early. Additionally, they aim to prevent death and enhance the quality of life among patients who have heart failure.
This has led to the creation of the FIND-HF algorithm, which uses records from 565,284 adults in the UK and 106,026 records provided by the Taiwan National University Hospital.
Research published in the International Journal of Heart Failure [5] further expands on the potential of AI and machine learning in heart failure. They explain that there are already developments that allow these AI models to help with the early detection and the monitoring of heart failure when connected to bedside monitors. This includes devices like ECGs, in which case AI can be used to detect subtle changes in heart function. This not only allows heart failure to be detected earlier, but also ensures more accurate monitoring of the progression.
The main benefit that comes with these models is the enhanced accuracy. AI could be able to detect changes that are often overlooked by the human eye; thus helping to ensure treatment can be initiated and additional tests can be done before heart failure causes complications.
Some studies have also shown potential in using these models for remote monitoring. This kind of research is still at an early stage, but is already showing promise in significantly advancing the way healthcare providers are able to keep updated with the progress of heart failure among their patients.
An interesting research paper was published in Heart Failure Reviews on Springer Nature Link [6]. This paper provides a detailed view of how remote monitoring in heart failure patients would work, and how artificial intelligence would actually use speech analysis as a primary element in helping to identify worsening of the condition. This is, once again, an indication of how AI can help to detect subtle alterations in heart function by constantly monitoring a patient’s speech remotely. Data can then be submitted to the healthcare provider, who can request an appointment with the patient should artificial intelligence pick up on something important.
Future Direction For Heart Failure with AI and ML
At the moment, the research surrounding the use of AI and machine learning models for heart failure patients is limited to bedside monitors. They use monitors such as ECGs in order to determine the overall functionality of the heart. However, since this research is still in an early stage, we can definitely say it looks promising when considering the future.
We will likely start to see artificial intelligence and machine learning technologies evolve even more. Considering the widespread adoption of portable hardware, we can also assume that these technologies will make their way to the wrists or in another compact form among heart failure patients. This will ensure the condition can be monitored uneven when the patient isn’t hooked up to machines.
As tech continues to evolve, we’ll also see improvements in using AI for the early detection of heart failure. This would allow providers to intervene before it becomes a more serious problem, potentially offering patients a much more positive prognosis.
AI will also provide a more personalized approach to implementing care services for patients. This means it could take into consideration factors like the patient’s genetics, medical history, current medication, and more. These factors would allow AI to use trained models to create customized treatments to help individuals with heart failure, based on the progression and current state.
We’ve already seen how AI makes it easier for remote monitoring of heart failure. This tech will continue to improve. Current interest lies strongly in using speech analysis for remote monitoring, but scientists are sure to find even more ways to help patients get a better understanding of their heart health, while also identifying changes in heart failure.
Conclusion
People with heart failure may experience various symptoms because the organ can’t pump blood effectively through the body. This deprives some areas of essential nutrients, as well as oxygen, with both being critical for normal functionality. New research shows that AI might be a promising tech to implement in cases of heart failure, where it can detect even subtle changes in heart function to ensure appropriate action is taken before complications set in.
References
[1] T. Yan, S. Zhu, X. Yin, et al. Burden, Trends, and Inequalities of Heart Failure Globally, 1990 to 2019: A Secondary Analysis Based on the Global Burden of Disease 2019 Study. Journal of the American Heart Association. 9 Mar 2023. https://www.ahajournals.org/doi/10.1161/JAHA.122.027852
[2] B. Bozkurt, T. Ahmad, K. Alexander, et al. HF STATS 2024: Heart Failure Epidemiology and Outcomes Statistics An Updated 2024 Report from the Heart Failure Society of America. Journal of Cardiac Failure. Jan 2025. https://onlinejcf.com/article/S1071-9164(24)00232-X/abstract
[3] M.G. Nicholls, A.M. Richards. Disease monitoring of patients with chronic heart failure. BMJ Heart. Apr 2007. https://pmc.ncbi.nlm.nih.gov/articles/PMC1861485/
[4] Using AI to detect heart failure. University of Leeds. 7 Jun 2024. https://www.leeds.ac.uk/news-health/news/article/5588/using-ai-to-detect-heart-failure
[5] M. You, J.J. Park, T. Hur, et al. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. International Journal of Heart Failure. 30 Nov 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10827704/
[6] J.D. Abraham, W.T. Abraham. Remote monitoring in heart failure: artificial intelligence and the use of remote speech analysis to detect worsening heart failure events. Heart Failure Reviews. 27 May 2025. https://link.springer.com/article/10.1007/s10741-025-10522-1