Processing...
The healthcare industry is facing mounting pressure to improve patient outcomes, reduce costs, and enhance operational efficiency. Traditional approaches are often insufficient to address these complex challenges, leading to a growing interest in innovative solutions like artificial intelligence (AI) and machine learning (ML). By leveraging the power of data analysis and predictive modeling, ML is transforming healthcare as we know it. The global healthcare AI market is projected to achieve a compound annual growth rate (CAGR) of 41.4% between 2020 and 2027, ultimately reaching an impressive $51.3 billion by 2027. This significant growth underscores the increasing role of AI in revolutionizing healthcare delivery and outcomes.
ML algorithms excel at identifying patterns and insights within vast amounts of data, enabling healthcare providers to make more informed decisions, personalize treatments, and streamline operations. This capability is particularly valuable in healthcare, where data is abundant but often underutilized. From predicting patient readmissions to accelerating drug discovery, ML is revolutionizing various aspects of the healthcare industry.
In this article, we’ll explore the transformative potential of ML in healthcare, examining its key applications and benefits. We’ll delve into specific use cases, showcasing how ML is already making a tangible impact on patient care, disease diagnosis, and treatment optimization. Join us as we uncover the exciting possibilities and navigate the future of healthcare powered by ML.
Machine learning in healthcare refers to the use of advanced algorithms and statistical models that enable computers to identify patterns, learn from data, and make predictions or decisions without being explicitly programmed. By processing massive volumes of individual patient data—ranging from electronic health records and lab results to images from medical devices—machine learning models help clinicians and researchers generate insights that would be difficult or impossible to detect manually. These insights power a wide range of applications, from diagnostics to administrative automation, all aimed at improving the efficiency and quality of healthcare delivery.
Machine learning is a subset of computer science and artificial intelligence (AI), with deep learning models being particularly effective in complex tasks such as image recognition and natural language processing. In the context of healthcare systems, these models can be trained to detect anomalies in radiology scans, monitor vital signs in real time, or personalize treatment plans based on patient-specific genetic or lifestyle data. This individualized approach forms the basis of precision medicine—a growing field that tailors treatments to each patient’s unique biological profile, significantly increasing the likelihood of better patient outcomes, especially for chronic diseases like diabetes or heart conditions.
The integration of machine learning into the healthcare industry is also transforming areas such as the drug discovery process, where algorithms can quickly sift through vast datasets to identify promising compounds for further testing. This reduces both time and cost in bringing new treatments to market. Across all these domains, machine learning in healthcare examples demonstrate how digital tools, grounded in robust scientific methodologies, are redefining what’s possible in modern medicine—enabling faster diagnoses, proactive care, and improved clinical decision-making.
AI, ML, deep learning (DL), and neural networks (NN) are often mentioned interchangeably, especially in the context of healthcare innovation. However, it’s important to understand that these are not competing technologies but rather layers within a hierarchical framework. At the top is AI, the broadest concept, referring to any technique that enables machines to mimic human intelligence—whether it’s decision-making, speech recognition, or visual perception. AI sets the stage for smarter healthcare systems by enabling technologies that assist in diagnostics, workflow automation, and patient engagement.
Machine Learning is a subset of AI focused specifically on creating models that improve automatically through data exposure. In healthcare, this has unlocked a wide range of practical applications—from predicting patient readmissions to automating insurance claim processes. These machine learning applications in healthcare have laid the groundwork for more advanced solutions that adapt to vast, ever-changing health data environments. Within this ML domain, many organizations are launching ml projects in healthcare to personalize treatments, manage chronic diseases, and streamline clinical operations based on pattern recognition and historical outcomes.
Deep Learning takes machine learning even further by leveraging neural networks—algorithms inspired by the human brain’s structure. These networks consist of multiple layers that allow systems to learn complex, abstract representations from massive data sets. For example, deep learning powers radiology tools that can detect tumors in CT scans with high accuracy or interpret ECGs without human oversight. Though DL is technically a subset of ML, its sophistication allows healthcare providers to handle unstructured data like medical images, genomic sequences, and clinical notes more effectively. Ultimately, each of these technologies contributes uniquely to the advancement of healthcare, and their collaborative use—not competition—is what drives the most impactful innovation.
Machine learning techniques can be applied to solve a wide variety of tasks. When it comes to applications of machine learning in healthcare, these tasks include:
Using machine learning for healthcare for tasks above can provide a lot of opportunities for healthcare organizations. First, it allows medical personnel to focus on patient care rather than spend their time on information search or entry. Also, machine learning has many advantages in clinical trials and research because it helps researchers access and process various data simultaneously.
The second important role of machine learning in healthcare is the increase in diagnosing accuracy. For example, machine learning has proven to be 92% accurate in predicting the mortality of COVID-19 patients.
Third, using machine learning in medicine can help to develop a more precise treatment plan. A lot of medical cases are unique and require a special approach for effective care and side effect reduction. Machine learning algorithms can simplify the search for such solutions.
Using machine learning in healthcare operations can be extremely beneficial to the company. Machine learning was made to deal with large clinical data sets, and patient files are exactly that – many data points that need thorough analysis and organizing.
Moreover, while medical professionals and a machine learning algorithm will most likely achieve the same conclusion based on the same data set, using machine learning will get the results much faster, allowing to start the treatment earlier.
Another point for using different techniques of machine learning for health is eliminating human involvement to some degree, which reduces the possibility of human error. This especially concerns process automation tasks, as tedious routine work is where humans err the most.
Clinical decision support tools help analyze large volumes of data to identify a disease, decide on the next treatment stage, determine any potential problems, and overall improve patient care efficiency. CDSS is a powerful tool that helps the physician do their job efficiently and quickly, and it reduces the chances of getting the wrong diagnosis or prescribing ineffective treatment.
This use of machine learning systems in medicine (healthcare) has been around for a while but has become more widespread in recent years. The reason behind this is the wider acceptance of the electronic health record system (EHR) and digitalization of various data points, including medical images.
Making sure that all the patient records are updated regularly is challenging, as data entry is a monotonous task. However, it is also crucial for effective decision-making and providing better care to patients.
One of the uses of machine learning in healthcare is using optical character recognition (OCR) technology on physicians’ handwriting, making the data entry fast and seamless. This data can then be analyzed by other machine learning tools to improve decision-making and patient care.
For the longest time, medical images, like X-rays, have been analog. This has limited the use of technology for anomaly identification, case grouping, and overall disease research. Fortunately, the digitalization of the process has led to more opportunities for these types of data analysis, including with the help of machine learning. According to a recent meta-analysis, algorithms of machine learning for health do the job as well as (and, in some cases, even better), human specialists, with 87.0% sensitivity and 92.5% specificity for the deep learning algorithms and 86.4% sensitivity and 90.5% specificity for human physicians.
One of the well-known successful examples of machine learning in healthcare is the InnerEye project from Microsoft. Its initial focus was on 3D radiological images, where ML tools were built to differentiate healthy cells and tumors.
What makes medicine such a complex and resource-heavy field is that every case has its specifics. People often have a sleuth of conditions that require simultaneous treatment. So, complex decisions must be made to construct an effective treatment plan, accounting for drug interactions and minimizing potential side effects.
How to use machine learning in healthcare to solve this problem? Well, IBM has figured that out with their Watson Oncology system that uses the patient history to produce multiple potential treatment options.
Prevention is as important in healthcare as disease treatment. One of the most important parts of preventive medicine is modifying one’s behavior to get rid of unhealthy habits and establish a healthy lifestyle.
One of the benefits of machine learning in healthcare is that it can be used to point out something we don’t notice. That’s exactly what Somatix does. This machine learning-based application makes the daily medical research of patients’ activity and points out their unconscious habits and routines so that they can focus on getting rid of them.
When it comes to the most dangerous diseases, identifying them in the early stages can raise the chances of successful treatment significantly. This also helps to identify the possibility of any potential worsening of the patient’s state before it happens.
One of the cases of the importance of machine learning in healthcare is that it can be used to successfully predict some of the most dangerous diseases in at-risk patients. This includes the identification of signs of diabetes (using a Naïve Bayes algorithm), liver and kidney diseases, and oncology.
One of the most important responsibilities for a physician is to gather a patient’s history properly. This can often be challenging, as the patient is not a specialist and doesn’t know which data is relevant to disclose.
Using machine learning in healthcare management, healthcare professionals can determine the most relevant questions they should ask a patient based on various indicators. This will help collect relevant clinical data and, at the same time, get a prediction of the most likely conditions.
Machine learning for health can help low-mobility groups (including the elderly and people using wheelchairs) improve their day-to-day lives with smart reminders and scheduling help, predict and avoid potential injuries by identifying common obstacles and determining the optimal paths, and acquire help as soon as needed.
While these solutions are effective, they are not as widespread as needed. However, healthcare and pharmaceutical companies already take steps to make them widely available. For example, in Japan, there is a plan to have 75% of elderly care performed by an AI.
Surgical procedures require great precision, adaptability to changing circumstances, and a steady approach for an extended period. While trained surgeons have all these qualities, one of the opportunities in machine learning for health is for robots to fulfill these tasks.
Right now, robotic surgery can be effectively used as a help for human surgeons. Namely, machine learning can be used for better surgery modeling and planning, evaluating the surgeon’s skills, and simplifying surgical tasks like suturing.
Based on the previously acquired data on active components in drugs and how they affect the organism, healthcare machine learning algorithms can model an active component that would work on another similar disease.
Such an approach can be used to develop a personal medication for patients with a unique set of illnesses or certain special requirements. In the future, this machine learning tool could be used in combination with nanotechnology for better drug delivery.
Clinical trials and research are costly and lengthy processes. There is a good reason behind this – new drugs and medical procedures should be proven to be safe before being used widely. However, there are cases when the solution needs to be released as soon as possible – like with the vaccines for COVID-19.
Fortunately, there is a way to make the process shorter with the help of machine learning algorithms. It can be used to determine the best sample for the trial, gather more data points, analyze the ongoing data from the trial participants, and reduce data-based errors.
The COVID-19 pandemic has shown us how unprepared we were for an infectious disease outbreak of this size. It is worth mentioning that experts in the area have warned the government about the possibility of such an event for years.
Now, we have tools based on machine learning that can help to detect the signs of an epidemic early on. The algorithms analyze the satellite data, news, and social media reports, and even video sources to predict whether the disease has the potential to grow out of control.
IBM Watson Health is one of the most recognized names in applying artificial intelligence technologies to healthcare. Their machine learning applications analyze structured and unstructured health data—including medical history, test results, and even doctors’ notes written in human language—to help identify patients who may be at risk for certain conditions. IBM’s tools are widely used to support oncology, cardiology, and genomics, offering insights that typically require human intelligence but at a much greater scale. By aiding medical professionals in early diagnosis and treatment planning, Watson helps enhance patient care and optimize resource allocation in hospitals.
DeepMind, a subsidiary of Alphabet, is known for developing deep learning models to assist in medical imaging and diagnostics. One notable project in collaboration with Moorfields Eye Hospital used machine learning to detect over 50 eye diseases with accuracy comparable to expert ophthalmologists. These ml projects in healthcare are helping to reduce healthcare costs by automating diagnostics that would otherwise require expensive specialists. Their work demonstrates the power of pattern recognition and data analytics in interpreting complex visual information from medical devices like retinal scanners.
Tempus uses machine learning to drive precision medicine in oncology and other chronic diseases. The company processes large volumes of clinical and molecular data to personalize treatment plans for patients. By employing machine learning engineers and data scientists, Tempus creates tools that identify patterns in patient health and treatment responses, helping physicians choose more effective therapies. This kind of targeted care not only enhances outcomes but also helps reduce unnecessary procedures, ultimately lowering healthcare costs.
PathAI applies machine learning to pathology, aiming to improve the accuracy of disease diagnosis through advanced image analysis. Their tools are trained using vast image datasets and are capable of identifying cancerous tissues with high precision. Machine learning applications like these improve medical practice by reducing diagnostic errors, especially in areas where pathologists are scarce. PathAI collaborates with major healthcare services and pharmaceutical companies, offering AI-powered solutions that support faster, more consistent diagnoses across patient populations.
Aidoc provides AI-driven solutions for radiologists by flagging critical conditions such as brain hemorrhages, pulmonary embolisms, and spinal fractures in real time. Their tools integrate into existing radiology workflows and help reduce the time to diagnosis during emergencies. With growing pressure on healthcare systems, especially in imaging departments, Aidoc’s machine learning applications in healthcare help doctors prioritize life-threatening cases, improving patient health outcomes while easing clinician workload and enhancing overall efficiency.
Babylon Health uses conversational AI and data analytics to provide accessible, affordable healthcare services through mobile apps. Their platform combines chatbots trained in human language processing with machine learning to offer symptom checking, medical triage, and virtual consultations. By interpreting patient inputs and cross-referencing them with vast medical databases, Babylon’s system can identify patients who may need further care or intervention. This model not only improves early detection but also contributes to reducing healthcare costs by minimizing unnecessary clinic visits.
The use of artificial intelligence has been a source of ethical dilemmas for a long time. However, some of them are specific to the use of machine learning in healthcare. Let’s review the most notable ones.
HIPAA and other privacy regulations ensure the security of the patient’s information. Everybody should have a right to keep information about their health private. Nevertheless, a lot of healthcare data leaks are happening every day that result in up to $16 million penalties for healthcare providers. However, data is the blood of the machine learning organism. How can these points effectively coexist?
This challenge is difficult to overcome. In most cases, machine learning doesn’t require a full spectrum of information on the patient (like name, email, phone number, and insurance policy number); thus, it can be effectively anonymized so that the person’s identity cannot be revealed, while the precision of the ML-algorithm won’t be discounted. For others, special data security approaches have to be implemented to ensure patient anonymity. If you want to learn more about security in healthcare software development – check out our special article on how to develop a HIPAA-compliant software.
Machine learning systems can be effectively used to help the elderly and people with psychological issues make decisions to improve their health. This concerns taking the right medications, creating healthy habits, and referring to the specialist whenever needed.
However, the ethical issue behind this is that people will potentially give up their autonomy and act as they are told. It limits their potential choice range to certain recommended options. So, a clear balance between the instructions from the algorithm and freedom of personal choice should be provided.
The decisions made by the machine learning algorithm completely rely on the data it has been learned on. If the input is unreliable or wrong, the result will be wrong as well. The flawed decision can harm the patient or even cause their death.
The ethical dilemma here is who would be responsible for the death of a patient because of the decision made by the machine learning technology? Right now, this remains an open question. The final decision on the treatment method is behind the patient, who should be informed about all the benefits and risks of each treatment method.
The algorithms of machine learning rely on data. The more relevant data is available, the better they work, and the more precise results and predictions could be achieved.
Many countries have legislation that restricts the use of patient data without their informed consent. So, the use of machine learning in healthcare should go hand-in-hand with informing the patient on it and the data security efforts applied to keep their data safe.
When developing a comprehensive healthcare software solution you should ensure its algorithms work effectively on a wide variety of patients. According to PMC, different ethnic and racial groups could have varying responses to pharmaceutical treatment and require special care. Thus, a machine learning solution should be “learned” on sufficiently wide and diverse patient cases and backgrounds. Moreover, it’s recommended to prevent and warn physicians about instances where the ML algorithm might lack research data and probably drive less accurate results, therefore.
The results you get from machine learning algorithms depend on the quality of data put into them. Unfortunately, medical data is not always as precise and standardized as it often needs to be. There are gaps in records, inaccuracies in profiles, and other difficulties.
Overall, electronic health records were not built to be used as a data source for an algorithm. So, before you apply a machine learning tool, you’d need to spend time gathering, cleaning, validating, and structuring data for its purposes.
There are multiple highly specific machine learning use cases that can help with patient diagnostics and treatment. Even if an ML tool works well on paper, it does not necessarily mean that it will be adopted by physicians. Thus, it’s critical to develop and roll out machine learning tools that would be intuitive and easy to use in the everyday medical workflow. Without the necessary feedback from people who will work with the tool, it will not be as effective, and the professionals will not trust it.
Besides hands-on healthcare specialists, an effective machine learning development team should include such roles.
Besides, it’s important to facilitate effective cooperation proceedings in the team so that it’s possible to deliver value and prove the product’s viability at the earliest opportunity. Learn more about effective ML team structure and processes here.
NIX is a software engineering company that can help develop a custom machine learning-based software solution for your healthcare goals on an end-to-end development cycle. Our technical knowledge and experience in the industry will help you reach your goals and bring your vision to life.
Among our successful projects in the area is the HIPAA-compliant app developed to improve patient care quality and engagement. Not only does it help users manage their medication plan, but it also helps to monitor the patient’s health remotely. If you need a similar solution or any custom healthcare ML tool, contact us right away.
Machine learning already has many effective uses in the healthcare industry, but it also has the potential to do a lot more. Besides patient diagnostics and treatment development, it can be used to improve medical care, predict outcomes, and even assist with surgeries.
While machine learning has a lot of potential in the healthcare industry, it also comes with certain challenges, like healthcare data quality, building physician-friendly products, and gathering a huge team of data experts. There also are certain ethical concerns, including patient safety and accountability. Despite certain challenges, the benefits of ML in healthcare outweigh them significantly. If you decide to harness ML for your healthcare organization, contact NIX to get an expert consultation on this matter and speed up time-to-value.
Be the first to get blog updates and NIX news!
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
SHARE THIS ARTICLE:
We really care about project success. At the end of the day, happy clients watching how their application is making the end user’s experience and life better are the things that matter.
AI-powered System: Cybersecurity Report Generation and Risk Mitigation
Healthcare
AI Assistant for Enterprise-grade Device Management
Internet Services and Computer Software
Manufacturing
NLP Chatbot for Accelerating Internal Operations
Smart Home System for Utility Management
Electronics
Real Estate & Property Management
Schedule Meeting
