​For a long time, the health care sector relied solely on humans to deliver patient care, utilizing their medical knowledge, experience, and physical observations. However, in recent times, health care has undergone a significant revolution thanks to the advent of machine learning, which enables more precise and effective treatments. There are several use cases for machine learning in health care.

One notable application of machine learning in health care is the development of predictive modeling. Predictive modeling involves training algorithms with past data, allowing them to identify patterns and make predictions using historical data. This facilitates the easy identification of patients at higher risk of specific diseases and suggests effective treatments based on historical data. For instance, machine learning can identify patients at a higher risk of developing sepsis by analyzing vital signs, laboratory values, and medical history.

Doctors can also leverage machine learning to enhance treatment accuracy. Machine learning models can analyze patient data and assist doctors in determining the most suitable treatment plan. A machine learning-based medical support tool can take into account factors such as a patient's age, weight, and allergies, guiding clinicians in selecting the most effective treatment for an antibacterial infection.

Machine learning also contributes to improving interactions between patients and doctors. This includes using chatbots and virtual assistants to promptly respond to patients' inquiries. By considering essential data like blood glucose levels or lung function, these chatbots can provide patients with personalized tips on managing their conditions. This can effectively reduce the workload of health care professionals.

Sanjiv Narayan, MD PhD, is a Professor at Stanford University who is an expert in machine learning in healthcare. He has researched and published on this topic for several years, in order to find better treatments for patients. His particular focus is on the use of machine learning for patients with heart rhythm conditions.

​Machine learning refers to a set of techniques and tools that enable computers to learn and adapt independently. It has found applications in various industries, such as enhancing social media, product recommendations, and image recognition.

Software engineers are leveraging machine learning algorithms to develop appealing features that enhance the user experience on social media platforms. For instance, machine learning algorithms on platforms like Facebook and TikTok can track a user’s activities, including chats, likes, comments, and time spent on specific types of posts. The data allows the algorithm to suggest suitable friends and pages for the user’s profile.

E-commerce websites use machine learning to analyze potential client behaviors based on past purchases, search patterns, and cart history. Machine learning algorithms can tailor product recommendations to each user's interests by tracking this information.

Furthermore, image recognition is a notable application of machine learning. This technique involves digitally cataloging and detecting features or objects in images. Advanced analyses like pattern recognition, face detection, and facial recognition use image recognition.

Dr. Sanjiv Narayan (@S_NarayanMD) is a recognized authority in the use of machine learning and artificial intelligence for medicine. He has written on "Use of Artificial Intelligence in Improving Outcomes in Heart Disease" on this link https://www-ahajournals-org.laneproxy.stanford.edu/doi/10.1161/CIR.0000000000001201 and also published extensively on "Machine Learning Primer for Medicine" as in this link. https://academic.oup.com/eurheartj/article/40/25/2058/5366208?login=true

​Ablation is one of the alternative ways to destroy liver tumors. It mainly targets small tumors, about an inch wide, without the need to remove them. It's used when surgery is not possible due to poor health and issues that reduce liver function. Although ablation has lower chances of treating cancer compared to surgery, it can be helpful to some patients or for those waiting for liver transplants. Different ablation techniques for the liver exist, with the variance mainly depending on the medium used to destroy the tumor.

One of the most common ablations for liver cancer, radiofrequency ablation (RFA), uses high-energy radio waves on small tumors. The doctor inserts a thin, long probe through a laceration on the skin to the probe and, guided by a computed tomography (CT) scan or ultrasonic aid, passes a high-frequency wave through the probe tip. The waves heat the tumor and destroy it. RFA procedure can be used solely to kill a tumor or as part of a more extensive operation.

Like RFA, microwave ablation (MWA) uses a probe under CT and ultrasound guidance to introduce microwaves that heat the tumor. However, microwave ablation has several advantages over RFA. MWA is faster and destroys cells faster, can be used for simultaneous tumor ablation, and works on both small and larger tumors, unlike RFA.

A second type of cryotherapy, cryoablation, destroys a tumor by freezing it. The doctor uses a cryoprobe to inject gas like liquid nitrogen, liquid nitrogen oxide, or compressed argon into the tumor. The tumor freezes and dies. Doctors also use ethanol ablation for liver tumors by injecting ethanol or concentrated alcohol. For this method, the patient sometimes requires multiple injections to destroy a tumor.

​Artificial intelligence (AI) has emerged as a pivotal technological advancement in healthcare. One of the most significant impacts of AI in medical research is its ability to enhance diagnostics.

Machine learning algorithms allow AI systems to rapidly and accurately analyze vast medical datasets, facilitating the early detection of diseases such as cancer, cardiovascular conditions, and neurological disorders.

Moreover, AI-driven approaches have ushered in personalized medicine, tailoring treatments to individual patients based on their unique genetic profiles, lifestyle factors, and medical histories. By employing predictive analytics and genomic sequencing, AI algorithms can predict a patient's response to specific therapies, enabling clinicians to prescribe targeted treatments with increased efficacy and fewer adverse effects.

Traditional drug discovery and development methods often face challenges such as lengthy timelines, high costs, and a high failure rate. However, AI algorithms offer a promising solution by swiftly identifying potential drug candidates and predicting their efficacy and safety profiles. By analyzing vast repositories of molecular data, biological pathways, and clinical trial records, AI-powered platforms expedite the identification of novel drug targets and optimize existing therapies.

In clinical settings, AI serves as a valuable tool for providing healthcare professionals with real-time decision support. By integrating patient data from electronic health records, wearable devices, and medical imaging modalities, AI algorithms assist clinicians in diagnosing illnesses, predicting disease trajectories, and recommending appropriate treatment regimens.

​In an era where technology is a part of every aspect of current existence, the fusion of healthcare and technology, known as digital health, has spurred groundbreaking advancements, transforming how we approach medical care. The COVID-19 pandemic accelerated the adoption of telemedicine, allowing patients to receive medical care remotely. As a result, research in telehealth technologies and remote patient monitoring has surged. Innovations in wearable devices, smartphone apps, and remote monitoring systems enable healthcare providers to remotely track patients' vital signs, manage chronic conditions, and provide timely interventions, ultimately enhancing patient outcomes and reducing healthcare costs.

Artificial Intelligence (AI) and machine learning algorithms are revolutionizing healthcare by unlocking insights from vast amounts of data. From diagnosing diseases to predicting treatment outcomes, AI-powered tools streamline decision-making processes and improve the accuracy of medical interventions.

Digital therapeutics are evidence-based interventions delivered through software platforms to prevent, manage, or treat medical conditions. These interventions often leverage cognitive behavioral therapy, mindfulness techniques, or therapeutic gaming to address various health challenges.

Moreover, advancements in genomics and biotechnology are driving the emergence of personalized medicine, tailoring treatments to individuals' genetic makeup, lifestyle factors, and environmental influences. Research endeavors in this domain aim to refine digital therapeutics platforms and harness genomic insights to develop targeted therapies for diverse patient populations.

​Sanjiv M Narayan, M.D., PhD, is an award-winning cardiologist who has worked in many positions in his medical career. A respected researcher, he is a professor of medicine at Stanford University. In San Diego, he gained recognition for his research on heart rhythm disorders. Dr. Sanjiv Narayan has developed outstanding bioengineering inventions in arrhythmia medicine through his research. He also keenly follows transformations in digital health, such as on-demand healthcare.

On-demand healthcare is one of the major tools of digital health. It enables patients to access online healthcare services in real time. On-demand healthcare aims to ensure patients can use their mobile phones from anywhere to access medical services quickly, easily, and remotely. Because of their busy schedules, patients want healthcare services when their schedules can accommodate them.

Due to technological advancement, patients are more willing to allow smart technologies, such as applications take a vital role in their medical care. As a result, waiting times have dropped, allowing efficiency and convenience.

​A cardiologist with medical experience spanning two decades, Sanjiv Narayan, MD, Ph.D., serves as a professor and researcher in the Department of Medicine at Stanford University. He also served the University of California in San Diego as co-director of electrophysiology. In addition, Sanjiv M. Narayan possesses extensive expertise in cardiovascular medicine.

Based on the National Health and Nutritional Surve resultsy, adults with type 2 diabetes have a high risk of undetected cardiovascular disease. Cardiovascular diseases affect the heart and blood vessels. In the study, researchers collected and analyzed blood samples of 10,300 adults between 1999 and 2004.

The researchers found that 1/3 of subjects with type 2 diabetes had markers of cardiovascular disease in their blood. These markers are characterized by high levels of certain proteins that typically increase due to stress or injury to the heart and cardiovascular system, pointing to a higher risk of heart attack, heart failure, and coronary heart disease. Examples of these marker proteins are N-terminal pro-B-type natriuretic peptide and troponin T.

In contrast, only 16 percent of subjects who don't have type 2 diabetes had elevated levels of the markers, suggesting a lower risk of cardiovascular disease in this group. The Journal of the American Heart Association published the study.

​A California-based cardiologist, Sanjiv M. Narayan, MD, PhD, is renowned for pioneering research in his field in San Diego, particularly in the area of atrial fibrillation, which he has dedicated decades of study to. He currently serves as a professor of medicine at Stanford University, where he leads a renowned center for patient treatment and research. With a focus on bioengineering, Dr. Sanjiv Narayan’s research explores various digital health trends.

One such phenomenon is connective digital care, which encompasses the various internet- and chip-enabled devices that collect and report patients' health information to assist diagnostics and health monitoring. Wearables, sensors, apps, and some digital tools that enable remote patient monitoring are in the purview of connective digital care. These tools are no longer reserved for only chronic diseases like cancer, and are assisting in the diagnosis and treatment of mild, acute, and chronic conditions. In the contemporary world of healthcare provider shortages, connective digital care is minimizing hospital visits and appointments to only necessary occasions, and also facilitating just-in-time medical intervention for life-threatening conditions.

While still in the experimental stages, AI in healthcare is also becoming more viable, as it autonomously analyzes data to identify patterns of diseases and disorders to guide diagnosis. These computer systems can interpret diverse data formats, including from imaging scans, wearable monitors, and sensors. AI can also make data-backed treatment recommendations to supplement provider care.

​Cardiologist Sanjiv M. Narayan, MD, earned his medical degree from the University of Birmingham in the UK before completing post-doctoral fellowships at Harvard and at Washington University. He later became director of a cardiac fellowship training program at UC San Diego and then assumed his current role as a professor and researcher at the Stanford University School of Medicine. Away from work, Dr. Sanjiv Narayan enjoys listening to piano music, which he finds calming.

The benefits of listening to any music have long been known. However, according to a September 2021 article in Scientific American, some believe that piano music in particular can teach medical students to learn about arrhythmias. Before introducing this new technique, history has provided several examples of how musicians made the connection between heart rhythms and music.

For example, up until the mid-19th century, the heartbeat was the standard unit of measure in musical time. Composer and theorist Franchinus Gaffurius wrote in his 1496 treatise Practica Musicae that the pulse of a healthy human was the proper measure of a musical beat.

Taking this idea and others, modern professors have taught medical students how to decipher abnormalities in heart rhythms as a part of cardiology education. For instance, nephrologist Michael Field taught students how to detect, through their stethoscopes, different arrhythmias using trills (rapid alteration between two notes), grace notes (ornamental notes that add expression to a piece), and decrescendos (decrease in loudness).

Concert pianist and mathematician Elaine Chew used music notation to pick up the signature rhythms of electrical abnormalities of the heart. She used Blue Rondo a la Turk (Brubeck) to capture the 2:4:3 rhythm of early ventricular beats; Le Grand Tango (Piazzolla) to capture arrhythmia of atrial fibrillation; and Little Etudes for Piano (with pedagogical descriptions from cardiologist Pier Lambiase) as a way to teach students about electrical heart disorders.