​The healthcare industry has experienced a significant transformation in recent years as digital technologies have expanded the possibilities for patient care beyond traditional clinical settings. Among these innovations, remote patient monitoring has emerged as a valuable approach to managing health conditions, supporting patient outcomes, and potentially reducing avoidable healthcare costs when implemented effectively.

Remote patient monitoring involves the use of connected devices such as wearable sensors, smartwatches, blood pressure monitors, glucose meters, and other health tracking technologies that collect patient data outside of hospitals and clinics. These devices continuously gather information about a patient's vital signs, physical activity, sleep patterns, and other health indicators. While collecting this data is important, the true value lies in the ability to interpret it effectively. This is where machine learning plays a critical role.

Machine learning algorithms can process vast amounts of health data in a timely manner, identifying patterns and trends that may not be immediately apparent to healthcare professionals. By analyzing historical and current patient information, these systems can detect subtle changes that may indicate the early stages of illness or deterioration. Early detection allows healthcare providers to intervene before a condition becomes severe, potentially preventing hospitalizations, emergency room visits, and costly medical complications.

Millions of people worldwide live with chronic conditions such as diabetes, heart disease, hypertension, and chronic obstructive pulmonary disease. These conditions often require continuous monitoring and regular medical attention. Machine learning systems can analyze data collected from monitoring devices to identify warning signs and predict potential health events. For example, changes in blood glucose levels, heart rate variability, or blood pressure patterns may indicate an increased risk of complications.

Machine learning can support patient safety by helping clinicians monitor large amounts of patient data without requiring constant manual review. Healthcare professionals cannot examine every data point generated by remote monitoring devices. Machine learning algorithms can help filter and prioritize information by identifying anomalies and generating alerts when potentially serious issues arise. This allows clinicians to focus attention on patients who may need immediate care while reducing the burden of monitoring large patient populations.

Remote patient monitoring powered by machine learning can also improve healthcare accessibility. Many patients live in rural or underserved areas where access to medical facilities and specialists may be limited. Through connected devices and intelligent monitoring systems, healthcare providers can track patient health remotely and provide care without requiring frequent in-person visits. This expanded access is especially valuable for elderly individuals, patients with mobility limitations, and those managing long-term conditions.

Many remote monitoring platforms provide patients with feedback about their health status, helping them better understand their conditions and treatment goals. Machine learning algorithms may generate personalized reminders, health recommendations, and motivational messages based on a patient’s behavior and progress. These features can support adherence to treatment plans, medication schedules, and healthy lifestyle habits, though their long-term impact depends on patient engagement and clinical follow-up.

Despite its many advantages, implementing machine learning in remote patient monitoring presents challenges. Healthcare organizations must address data privacy, cybersecurity, and regulatory compliance. Large volumes of sensitive health information must be protected against unauthorized access and potential breaches. Healthcare providers also need to evaluate model accuracy, improve transparency, monitor performance, and reduce biases that could affect patient care decisions.

As technology continues to evolve, the role of machine learning in remote patient monitoring is expected to expand further. Advances in artificial intelligence, wearable technology, and data analytics will enable even more sophisticated monitoring capabilities. Future systems may provide increasingly accurate predictions, support earlier interventions, and offer highly personalized healthcare experiences.

​Heart rhythm disorders, also known as cardiac arrhythmias, occur when electrical signals fail to coordinate heartbeats, resulting in rhythms that are too fast, too slow, or irregular. These disorders affect millions of people in the United States, with some estimates placing the figure at up to 5 percent of the population. If left untreated, they can lead to serious complications such as stroke, heart failure, or cardiac arrest.

The heart functions as a mechanical pump regulated by electrical signals that travel through specialized pathways to trigger muscle contractions. These signals ensure the upper and lower chambers work together to distribute blood throughout the body.

For most healthy adults, a normal resting heart rate ranges between 60 and 100 beats. Disorders arise when the heart's electrical wiring system malfunctions and fails to generate proper timing. Consequently, the organ may lose its ability to pump blood efficiently.

Cardiac arrhythmias are categorized based on where they begin and whether they make the heart beat too fast or too slow. Atrial fibrillation (AFib) is the most common arrhythmia, affecting more than 12 million Americans. This condition is marked by very rapid activity in the upper chambers, sometimes over 400 beats per minute, and poor coordination between the upper and lower chambers. It can keep the lower chambers from filling completely and increase the risk of blood clots and stroke.

Other common disorders include bradycardia, where the resting rate drops below 60, and tachycardia, which exceeds 100 pulses per minute. Atrial flutter involves a very fast – up to 350 beats per minute – but organized rhythm in the upper chambers. Lastly, ventricular fibrillation is a life-threatening emergency where the lower chambers quiver – rapid and uncoordinated heart muscle shaking or twitching – instead of pumping, necessitating intervention using a defibrillator.

Various health conditions and lifestyle choices contribute to irregular heart rhythm development. Among these, advancing age and genetic history increase the likelihood of experiencing these disorders. Other factors include existing heart diseases, such as valve abnormalities and hypertension.

Furthermore, chronic conditions like sleep apnea, diabetes, and thyroid dysfunction can trigger electrical disruptions. Everyday habits, including alcohol, tobacco, and caffeine consumption, also play a role by destabilizing the heart's rhythm. Managing these factors is critical in preventing arrhythmias.

Symptoms of an arrhythmia include experiencing a pounding chest or skipped beats. Dizziness, lightheadedness, and temporary loss of consciousness are other signs. Furthermore, breathing difficulties and sudden weakness may be signs that the heart is struggling to pump oxygen-rich blood. However, many patients remain asymptomatic, meaning that a healthcare provider may only discover the issue during a routine physical examination.

Several methods and tools help diagnose arrhythmias. An electrocardiogram (ECG) is the primary tool for recording the heart's electrical activity. A Holter monitor, which is a wearable ECG device, helps track the pulse during daily activities. Stress tests observe how the heart reacts to physical exertion on a treadmill or bicycle. An echocardiogram shows the heart’s structure and blood flow using sound waves, while an electrophysiology study examines the heart’s electrical activity to detect rhythm problems.

Treating cardiac arrhythmias focuses on restoring normal rhythm or regulating heart rate to prevent complications. Patients are usually advised to start with heart-healthy lifestyle adjustments, such as adopting a nutrient-dense diet, reducing stress, and increasing physical activity.

Medications provide the next level of care by controlling heart rates or preventing dangerous blood clots. Similarly, procedures like cardioversion use electrical shocks to reset the heart's natural rhythm. Another intervention, catheter ablation, uses heat or cold to scar small heart tissue locations that produce faulty signals. For permanent management, specialists may implant pacemakers to correct slow beats or defibrillators to stop life-threatening rhythms. Finally, a surgical option like the MAZE procedure creates scar tissue patterns to block irregular electrical paths.

​Cardiac mapping is a diagnostic process that creates detailed visual maps of the heart’s electrical activity to help identify the cause of irregular rhythms, called arrhythmias. It is closely associated with electrophysiology studies. Doctors use these maps to identify anatomy, tissue characteristics, and abnormal electrical pathways. This information helps guide treatment planning.

Physicians use cardiac mapping to diagnose arrhythmias such as atrial fibrillation or ventricular tachycardia, which manifest as palpitations, exhaustion, or fainting spells. Mapping helps locate specific regions where electrical systems malfunction, resulting in these symptoms. Clinicians also use this process to predict the risk of cardiac death.

When doctors pinpoint the source of abnormal electrical activity, they can create more targeted treatment plans and focus procedures on the tissue most likely to be causing the arrhythmia. Mapping can also help specialists distinguish between different rhythm disorders. It is an important tool in planning interventions such as cardiac ablation.

There are two broad approaches to cardiac mapping: invasive and non-invasive. Both can provide more detail than a standard electrocardiogram (ECG). In invasive mapping, a flexible catheter is guided into the heart to record electrical activity from different areas. As the catheter touches parts of the heart wall, the system collects data points and updates the map. Different colors on the display can represent electrical features, such as when an area activates.

High-density mapping captures thousands of these points in minutes. It is a beat-by-beat system that shows how electricity travels and spreads over healthy and diseased heart zones. Other advanced tools include high-resolution tools that provide a microscopic view into heart cell activity, and motion compensation algorithms, which keep measurements stable despite patient breathing or movement.

Non-invasive systems use a specialized vest with 252 electrodes to record heart activity while the patient undergoes a computed tomography (CT) scan. This approach provides a global view of electrical behavior across the heart at the same time. Non-invasive mapping can be useful in planning complex ablation strategies.

Cardiac mapping helps guide ablation procedures used to correct irregular heart rhythms. Ablation uses heat, such as radiofrequency energy, or cold energy, called cryoablation, to create small scars in targeted heart tissue. These scars block abnormal electrical signals. Mapping helps doctors identify the areas most likely to require treatment, allowing ablation to be performed with greater precision.

Preparation strategies mostly affect the invasive procedure. Before the process, one must fast for at least eight hours. A doctor reviews all medications and may stop a patient from using blood thinners to prevent the risk of bleeding. Patients often require sedation or general anesthesia for comfort during the mapping process.

After the procedure, a person must lie flat for several hours to protect the catheter insertion site. There are no serious risks, but in rare cases, there can be infection, blood clots, or damage to heart valves. Soreness at the incision site is common for a few days during recovery.

Current advancements in cardiac mapping focus on high-definition resolution and artificial intelligence (AI) integration. Grid-based sensors can provide clear signals in difficult anatomical locations. AI and mathematical modeling help analyze global electrical maps faster than traditional methods. These innovations reduce the need for repetitive chamber surface scans during complex procedures.

Furthermore, electrophysiology, an enhanced mapping technology, allows for personalized medicine based on an individual patient's anatomy. This innovation allows clinicians to see electrical abnormalities that were previously invisible during standard testing. This progress leads to safe interventions and high success rates in managing arrhythmias.

References:
Narayan SM, John RM.. Advanced Electroanatomic Mapping: Current and Emerging Approaches. Curr Treat Options Cardiovasc Med. 2024 Apr;26(4):69-91. doi: 10.1007/s11936-024-01034-6. Epub 2024 Feb 27. PMID: 41104347

​Atrial fibrillation is an irregular, rapid, and potentially life-threatening heart rhythm that complicates blood flow from the heart, increasing the risks of developing strokes, blood clots, and heart failure. Exciting technology is reshaping how atrial fibrillation is detected and monitored, shifting this care effort from occasional, clinic-based evaluation to continuous, real-world tracking (1).

​Atrial fibrillation is often intermittent and asymptomatic, making it challenging to detect using traditional tools such as an electrocardiogram, which captures irregular heart rhythms in limited recording windows, meaning episodes can easily go unnoticed. As such, a significant number of patients remain undiagnosed until complications, such as stroke and heart failure, occur.

​Several digital health technologies are addressing this gap by enabling long-term, continuous monitoring (2). Wearable devices, mobile health applications, telemedicine, and artificial intelligence (AI) now enable the passive collection of heart rhythm data over extended periods. This shift is significant because atrial fibrillation does not always occur during clinical visits, so frequent monitoring increases the likelihood of detecting irregular episodes and provides an accurate picture of a patient’s heart rhythm.

​Wearable devices, particularly smartwatches and fitness trackers, are important to this transformation. These devices rely on sensors, such as single-lead electrocardiography, to monitor one’s heart rate and rhythm in real-time. Researchers explain that smartwatch-based detection can achieve a significantly high specificity rate, making it effective in monitoring efforts. This accuracy level, coupled with the ease of use, allows individuals to continuously track their heart health without disrupting daily routines.

​Beyond convenience, wearable technology enables early detection. Some devices can identify irregular heart rhythms before symptoms appear, prompting users to seek medical evaluation. Researchers argue that modern fitness trackers can detect signs of atrial fibrillation by analyzing heart rate variability and rhythm irregularities, approaching the diagnostic capabilities of electrocardiography (ECG) systems. Importantly, these devices are not intended to replace professional diagnosis, but rather serve as valuable screening tools that bridge the gap between patients and care providers.

​Mobile health applications expand the capacities of wearable devices by providing platforms for data storage, visualization, and communication. These applications collect heart rhythm data and present it in a format that is easy for patients and clinicians to interpret. Some applications also generate alerts when abnormal patterns are detected, enabling timely medical intervention. Experts explain that integrating ECG-based systems with mobile applications can achieve high accuracy, improving specificity and sensitivity in detecting atrial fibrillation.

​Telemedicine is another important innovation in detecting and monitoring atrial fibrillation. With interconnected devices, clinicians can monitor patients’ heart rhythms in real time without the need for frequent hospital visits. This is especially beneficial for those in remote and underserved regions. Continuous data transmission helps caregivers detect abnormalities early and promptly adjust treatment plans, reducing the burden on healthcare systems by minimizing hospital visits and efficiently utilizing resources.

AI is playing an increasingly important role in enhancing these technologies’ accuracy and predictive capabilities (3). AI algorithms analyze large volumes of heart rhythm data, identify important patterns, and distinguish between normal variations and clinically significant abnormalities. Modern AI-powered wearables, for instance, can detect atrial fibrillation and predict arrhythmia episodes before they occur, offering a critical window for preventive action.

Despite these advances, integrating technology in detecting and monitoring atrial fibrillation faces several challenges, including accuracy. Factors such as poor sensor contact and user error can affect data quality and lead to the generation of false-positive and false-negative results. Some studies highlight limitations in smartwatch-based screening, including variability in performance across different devices and populations, while other researchers argue that false alarms can evoke unnecessary anxiety, mandating additional testing and increasing healthcare costs.

There is a need to ensure that these technologies reach all people in need (4). ​Lastly, there are data privacy and security concerns in leveraging technology. Frequent monitoring efforts collect large amounts of sensitive health data, raising concerns about how this information is stored, shared, and protected. Complying with data protection regulations and implementing robust cybersecurity measures is, therefore, essential for the widespread adoption of technology in detecting and monitoring atrial fibrillation (5).

1. https://www.ahajournals.org/doi/full/10.1161/CIRCEP.124.012939
2. https://academic.oup.com/europace/article/27/5/euaf071/8100285?guestAccessKey=
3. https://www.ahajournals.org/doi/full/10.1161/CIR.0000000000001201
4. https://www.nature.com/articles/s41569-025-01184-5
5. https://www.nature.com/articles/s41746-025-01520-6

​Cardiac care is shifting away from generalized protocols toward individualized planning. In arrhythmia therapy, computational models now guide decisions based on each patient's unique anatomy and electrical patterns. These simulations predict how different treatments affect rhythm stability, helping clinicians prepare more accurately.

Conventional treatment strategies for arrhythmias often rely on empirical approaches. Standard ablation patterns and device protocols may miss key anatomical differences or less obvious conduction routes. In many cases, this uniformity limits effectiveness or leads to unnecessary interventions. Personalized modeling offers an alternative by creating digital replicas of the heart that reflect a person’s specific structure and rhythm profile.

Digital heart simulations combine imaging, mapping, and clinical input to trace the heart’s electrical activity in a personalized format. The resulting digital environment allows physicians to test procedural strategies virtually, revealing how different interventions may affect conduction or trigger recurrence. This ability supports more precise planning and lowers procedural risk.

Personalized simulations have shown particular value in atrial fibrillation, where reentrant pathways are complex and variable. Models help reveal areas of sustained conduction or vulnerable circuits that professionals may not detect through traditional mapping. Using simulation-guided plans, clinicians shorten procedures and deliver lesions more accurately, lowering recurrence and enhancing long-term results.

Device therapy also benefits from modeling. Simulations can test how pacemakers or defibrillators perform under different cardiac conditions, allowing care teams to refine placement and settings before surgery. This approach supports better long-term outcomes and minimizes complications such as lead dislodgment or suboptimal pacing. Anticipating conduction outcomes before implantation helps reduce the need for post-procedural corrections or secondary interventions.

Beyond modeling devices in isolation, some programs integrate these simulations with real-time data from EP labs. By feeding updated mapping inputs into prebuilt models, clinicians can adjust plans mid-procedure and respond more quickly to patient-specific feedback. This adaptability reinforces modeling as a dynamic tool, not just a static planning resource.

The collaboration behind simulation-driven care brings together imaging experts, computational engineers, software specialists, and electrophysiologists. Integrated programs ensure that these technologies operate reliably in daily clinical settings. Cross-disciplinary training strengthens this process, preparing professionals to interpret simulations with technical and clinical fluency.

Maintaining accuracy in personalized modeling requires continuous validation. Professionals must compare clinical outcomes with predicted results to adjust assumptions and refine algorithmic logic. This iterative process ensures simulations remain anchored in real-world scenarios rather than theoretical constructs.

This modeling approach also enhances patients' engagement with their treatment plans. Personalized visuals help clarify complex procedures and support discussions around risk, benefit, and choice. Transparent communication about how simulations inform treatment enables shared decision-making and builds confidence in therapeutic plans. When patients understand the reasoning behind their care, adherence to follow-up protocols and trust in medical recommendations improve.

Despite their promise, simulations are not yet accessible in all environments. They require infrastructure, expertise, and time that may not be available in community settings. Expanding access will depend on scalable systems, institutional investment, and continued research into automation and usability. Partnerships between academic centers and healthcare networks could help broaden implementation through training and shared resources.

Simulation-based planning complements clinical judgment by enhancing precision and anticipating variation. Individualized simulations enable safer, more targeted interventions reflecting each patient's needs. As this technology continues integrating into electrophysiology, it marks a clear step toward more responsive and evidence-informed arrhythmia care.

​A new medical tool powered by artificial intelligence (AI) has simplified heart monitoring processes for medical professionals and patients. A Scripps Research initiative has demonstrated that AI-supported medical tools can help cardiologists diagnose heart attacks and related cardiovascular conditions using electrocardiogram technology that is simpler and more accessible to use.

In the past, physicians and surgeons have largely relied on 12-lead electrocardiograms (ECGs) to research and diagnose heart failure and abnormal heart rhythms. This poses several challenges due to the complex setup involved with 12-lead ECGs; each lead is a long wire attached to electrodes that must be placed at certain points on a patient's body, including their chest and limbs. The bulky, specialized equipment is hard to move and can only be operated by trained experts, limiting use among many clinicians and forcing patients to travel significant distances for 12-lead ECG analysis.

The introduction of AI support has allowed medical researchers to develop similar equipment that uses only three electrodes, making the process much simpler and more mobile. The initial report, first published in a 2024 issue of npj Digital Medicine, described the new setup's ability to produce reports on par with those yielded by 12-lead ECGs. The advent of such technology would provide countless patients with access to high-quality, time-sensitive data on their cardiovascular health. The new setup is also expected to decrease the cost of ECG technology.

The research team, headed by cardiologist Evan Muse, MD, PhD, centered the new equipment around an AI tool that draws on data from more than 600,000 12-lead ECGs collected from a diversity of patients. About half of the patient population used by the AI tool demonstrated healthy heart rhythms, while the remaining group lived with a wide range of cardiovascular conditions. Giorgio Quer, PhD, the director of artificial intelligence at Scripps Research Translational Institute, harnessed that data and began experimenting with two and three-electrode setups accurate enough to reflect a full 12-lead analysis.

Researchers initially wanted to limit the equipment to leads on patients' limbs, as these are the easiest electrodes for non-experts to work with. The team did not want to compromise accuracy or patient safety, however, and determined that much of the strongest data was collected when using a chest lead as well.

After devising the overall setup, the research team examined a set of 238 ECGs, 50 percent of which demonstrated signs of heart failure. Cardiologists were then asked to assess the ECGs, which provided researchers with two key successes. First, the cardiologists could not determine which ECGs came from the standard 12-lead setup and which ECGs had been partially constructed from AI data using three leads. Furthermore, cardiologists correctly identified heart failure symptoms in 81.4 percent of the AI-produced ECGs, just shy of the 84.6 percent on the original 12-lead ECGs.

While the AI tool has produced encouraging results, more studies using a wider array of patient groups will need to be completed. That said, the three-lead setup promises faster diagnoses and treatments for patients who would otherwise not have access to such technology. The technology is just one example of AI tools being used to screen for and diagnose different heart conditions, including atrial fibrillation.

​HRX 2025 has been scheduled for September 4-6 at Signia by Hilton Atlanta in Georgia. Other upcoming meetings are the American Heart Association AHA Scientific Sessions in November 2025 in New Orleans. As in years past, the conference will bring together global leaders and innovators committed to defining future generations of healthcare. Recent innovations in the field of cardiovascular medicine have included aortic valve replacements, protein inhibitors, and mitral valve clips.

The aortic valve is a critical component of cardiovascular health. Located directly adjacent to the left ventricle, the valve prevents blood from flowing in the wrong direction between the ventricle and the aorta. Without a properly functioning aortic valve, blood can leak back into the left ventricle, leading to various health complications.

Aortic valve stenosis (AVS) is a particularly damaging condition that occurs when the aortic valve narrows, preventing an adequate flow of blood. Up to 300,000 Americans receive AVS diagnoses each year, per the Frankel Cardiovascular Center at the University of Michigan Health. In the past, severe cases of AVS demanded open heart surgery, allowing surgeons to repair or replace the valve. Unfortunately, AVS typically impacts older patients who are not good candidates for such intensive, complex procedures.

More recently, healthcare innovators have developed the process of transcatheter aortic valve replacement (TAVR) as a minimally invasive alternative to surgical intervention for AVS. The procedure requires physicians to make a single small incision by the groin or chest, use the incision to place a catheter into an artery, and harness medical imaging technology to guide a replacement valve through the artery to the damaged aortic valve.

In addition to the procedure itself, TAVR often replaces damaged aortic valves with replacement valves constructed from heart tissue harvested from cows or pigs. Studies have indicated that TAVR is not only a comparable alternative for high-risk patients but, at times, a superior treatment plan for low-risk patients living with advanced AVS.

Another recent advance in the field of cardiovascular health and medicine involves a class of drugs designed to inhibit the SGLT2 protein. These inhibitors were initially developed to reduce blood sugar levels in patients living with type 2 diabetes. The inhibition of the SGLT2 protein reduces the body's ability to reabsorb glucose into the kidneys.

While SGLT2 inhibitor drugs proved effective for type 2 diabetes patients, the drugs demonstrated the ability to help even more people, namely those at risk for heart failure or kidney disease caused by high blood pressure. In addition to decreasing blood pressure levels, SGLT2 inhibitors further reduce the heart's workload by lowering plasma volume and limiting activity throughout the sympathetic nervous system. 

The results of a series of trials were published in The Lancet in 2022, finding the SGLT2 inhibitors "significantly reduce the risk of mortality," particularly when integrated into traditional heart failure therapy. Three inhibitors have received Food and Drug Administration approval to reduce the risk of heart failure.

Finally, MitraClip functions as a less invasive alternative to open-heart surgery for mitral regurgitation, a condition impacting nearly two percent of all Americans and over nine percent of Americans over the age of 75. Mitral regurgitation occurs when the mitral valve does not fully close, allowing blood to flow back into the heart. Symptoms of the condition range from shortness of breath to loss of consciousness; more severe cases can lead to atrial fibrillation and heart failure.

Similar to the TAVR procedure, MitraClip involves doctors guiding a catheter through a vein to the patient's heart, at which point the catheter is used to install a small clip that helps the valve close more securely. The surgery can be completed in as little as 60 minutes and has proven to be both safe and effective for patients.

​Artificial Intelligence (AI) has changed various healthcare fields, with cardiovascular medicine benefiting as well. In this domain, AI helps tackle once-persistent challenges such as diagnosis accuracy and surgical safety and outcomes, as well as brings key improvements in different areas.

AI has streamlined cardiovascular disease and stroke imaging, largely circumventing past image processing and interpretation challenges. AI-powered tools make diagnosing and prognosis of cardiovascular conditions easier by automating abnormality detection, measuring plaque and blood flow, and predicting sudden cardiac death or stroke risk. AI also offers quick automated measurements for procedures, cutting down treatment time. Still, institutions using AI for medical imaging should be cognizant of the challenges tied to acquiring data and organizing and labeling it, as well as address ethics, bias, and data quality concerns.

Electrocardiography (ECG), a medical test tracking heart rhythm, has also changed with AI. With too few experts to interpret the growing number of ECGs, AI offers a solution by automating this task. Current ECG interpretation devices are rule-based and have limits. However, AI and machine learning (ML) can read ECGs more like human experts, leading to better care decisions. Sometimes, AI can read ECGs better than specialists, spotting hidden heart problems and finding conditions like aortic stenosis (narrowing of the heart's aortic valve opening), something traditional methods often miss.

When paired with photoplethysmography (PPG), а light-based method to track blood flow in the skin, AI can spot atrial fibrillation (AF), an irregular heartbeat. Smartwatches and other wearables with PPG can catch AF in real time, even outside clinical settings. By analyzing the timing and shape of the PPG signal, AI algorithms can differentiate between AF and a normal heart rhythm. This setup is more efficient than older methods like regression models and manual feature extraction, which lack precision. Complex AI models can also automatically find critical patterns in the PPG signal.

In hospitals, AI and machine learning boost bedside monitoring. For years, bedside monitoring has relied on pre-set rules to trigger alarms when a patient's vital signs exceed a specific range. But these systems often give false alarms and look at each vital sign in isolation, often missing the connections between them. AI-based tools can simultaneously watch all the data from monitors and see subtle patterns across many vital signs. This approach gives a fuller picture of patient health and reduces false alarm detection.

Research highlights ways AI improves hospital monitoring beyond sorting true from false alerts. For instance, AI models can spot small physiological changes indicating a patient is getting sepsis, where the body overreacts to infection. AI tools also help doctors act quickly by predicting when a patient might experience cardiac arrest in the hospital. Moreover, they assess surgery risks by weighing various patient factors before, during, and after procedures, predicting complications and mortality, and guiding anesthetic drug dosing.

AI helps researchers and doctors study heart disease genes to create better treatments. For example, genome-wide association studies (GWAS) use AI to scan the entire genome, spotting patterns and links between genes and specific heart conditions. AI and machine learning methods such as gradient boosting sharpen polygenic risk scores (PRS), which gauge a person's genetic odds of developing cardiovascular disease. Training AI on diverse, multi-ethnic genomic data makes these models work better for different groups. AI also connects observable traits, or phenotypes, to genes by analyzing facial images to detect genetic syndromes tied to heart issues.

Teamed with intelligent robots, AI boosts precision in complex surgeries while keeping them less invasive. The da Vinci Surgical System, for instance, enables robot-assisted surgeries, reducing risk and shortening hospital stays. For heart procedures like coronary interventions, AI robots let doctors work from a safe distance, avoiding radiation exposure.

​Computational Medicine in the Heart (CHIP) is a T32 Program of the National Institutes of Health (NIH) National Cancer Institute (NCI). The descriptor T32 applies to all NIH Ruth L. Kirschstein National Research Service Awards, which support doctoral research training in various areas.

CHIP T32 is an integrated program of the Stanford Cardiovascular Institute (CVI) of Stanford Medicine. The program supports training for post-doctoral fellows interested in using computational medicine to address unmet patient needs in cardiovascular (CV) diseases. CHIP T32 provides two years of training for six fellows who choose their mentors from 38 participating faculty members in the School of Medicine, School of Engineering, or School of Humanities and Sciences.

In addition to interdisciplinary research and coursework, CHIP T32 training comprises professional development and team-based work. Beyond the CVI and the specific schools listed above, content for CHIP T32 training comes from Stanford’s Institute for Computational and Mathematical Engineering and Artificial Intelligence in Medicine and Imaging.

​The healthcare industry continues to see development. Technology, such as machine learning (ML), provides useful ways to leverage patient data and help medical professionals deliver effective care.

Machine learning is a specific variant of artificial intelligence (AI) that enables systems to learn from data and detect patterns with minimal human intervention. AI helps computers and machines reason, learn, and act like human intelligence or sift through large data sets. Therefore, machine learning allows systems to analyze data, recognize patterns, and make decisions with minimal human input.

In healthcare, machine learning helps medical professionals extract meaning from complex medical information and patient data. Medical professionals have applied it in diagnostics as algorithms analyze medical images, such as X-rays, MRI scans, and CT scans, by detecting patterns that indicate specific diseases or conditions. These algorithms can support doctors by providing faster, more accurate diagnoses, ultimately leading to better patient outcomes.

Moreover, machine learning has aided drug discovery and development. ML models can identify previously unknown side effects or drug interactions, enhancing drug safety and efficacy. Pharmaceutical companies and healthcare organizations also use it to accelerate drug development and improve treatment effectiveness.

Lastly, machine learning can potentially reduce costs, streamline operations, improve data security and privacy, and enhance overall patient care. By automating routine tasks, optimizing workflows, and analyzing large datasets, healthcare providers can offer more personalized and efficient patient care.