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Machine learning in healthcare contributes to more streamlined, effective, and organized performance of numerous medical procedures and assists doctors in their daily operations, thus bringing more personalized and proactive care to patients.
The growing role of machine learning in healthcare
The current challenges that the industry faces are the reason behind the growing adoption of machine learning and its rapid integration in clinical decision support. These challenges include the increasing demand for qualified personnel, lengthy drug discovery, or inability to recognize certain diseases at early stages. In the light of this, here are some numbers that clearly show how machine learning is becoming an integral part of healthcare:
- The global AI healthcare market is expected to reach $36.96 billion, with a CAGR of 38.6% compared to the numbers from 2024;
- 76% of AI-enabled medical devices that are authorized for sale in the US are used in the radiology field;
- 47% of healthcare organizations use or plan to use AI-powered virtual assistants;
- In 2025, 340+ FDA-approved AI tools are already being used for diagnostic purposes.
Main benefits of machine learning in healthcare
Task automation
Implementation of machine learning can automate a number of routine tasks and processes, especially in the field of hospital management. Examples of such tasks include collection of patient data or staff scheduling as well as generation and processing of medical documents. As for real-life examples, Nuance’s Dragon Medical One uses AI to capture voice-generated content right into medical systems, reducing the documentation time up to 50%.
Improved patient care
Machine learning is the cornerstone of precision medicine, aimed at delivering personalized care to every patient through careful analysis of their medical history and genetic profile. Also, ML contributes to more accurate diagnosis-making and more effective medical image analysis. AI models, designed in collaboration between the Massachusetts General Hospital and MIT, displayed a 94% accuracy in lung nodule detection, greatly outperforming human radiologists.
Better prediction of health risks
In relation to the point above, ML models not only help detect warning symptoms but also contribute to earlier health risk prediction, allowing doctors to start treatment in a proactive manner. This approach is much more beneficial for patients as it allows mitigating the disease development at early stages before it causes significant harm. Moreover, machine learning can help predict not only the disease development but the state of the patients and whether they will require specialized care in the future. Back in 2020, several US hospitals used an ML-powered solution that predicted ICU transfers within 24h. The solution proved to be of great benefit as the hospitals were overloaded due to the COVID-19 pandemic and medical professionals were not able to treat each patient efficiently enough.
Key applications of machine learning in healthcare
1. Disease prediction and diagnosis
Accurate disease prediction remains one of the biggest challenges in healthcare, and machine learning is used to facilitate and automate the process to a certain extent. ML-based image recognition is one of the most common use cases of this technology in diagnostics since the ML model can recognize and detect patterns that were previously unknown or unnoticed by medical professionals. This, in turn, allows early detection of diseases and their timely prevention as well as the patient outcome prediction, increasing the chances for successful treatment.
Also, don’t forget about predictive analytics that can be used to forecast the potential disease development or for hospital readmission prediction. Machine learning models used in forecasting the outcomes of general surgeries, displayed an impressive level of accuracy, with 72% of accuracy for SSO predictions and 84% of accuracy for 30-day readmissions.
Such forecasts are created based on the historical data of the patient combined with the newly collected one and significantly help doctors in analyzing the disease flow and the possibility of its development.
2. Medical imaging
Medical images include X-ray scans, MT, and MRI scans and help medical professionals detect diseases at various stages of development. Unfortunately, medical imaging depends heavily on one’s ability to analyze these images properly, and hence, the chance of a human error remains quite high. For example, the American Cancer Society notes that screening mammograms miss about 1 in 8 breast cancers (~12.5%), underscoring the persistence of human/technical misses in real programs.
The use of machine learning in medical imaging eliminates the possibility of human error and can detect even the slightest alterations from the norm with up to 90% accuracy, thus enabling faster anomaly detection and prevention of illnesses from progressing. For example, the cancer detection rate increased by 17.6% from using AI in CT scans analysis, as reported by Alexander Katalinic from the University of Lübeck (Germany).
4. Personalized treatment
Personalized medicine, also known as precision medicine, is on the rise these days due to the advancement of machine learning. This practice is aimed at closely studying one’s genetic profile and medical history in order to select the most suitable treatment and to accurately predict how a specific patient will respond to certain drugs and treatment. This approach to patient care significantly increases the chances of a patient for positive immune response and successful outcome of the treatment. In this use case, machine learning helps medical professionals quickly and effectively analyze the patient’s data and detect which drugs and treatment plans would be the most compatible.
5. Drug development and discovery
Another big challenge of the healthcare industry is the process of drug discovery and development and clinical trial optimization. First, it is very expensive, and second, it might take years to develop a single drug. Machine learning greatly assists here by speeding up the process of finding combinations of components by 50% and drawing accurate predictions (with over 90% accuracy) on how a specific drug will perform. Doctors can also use ML to identify new use cases for already existing drugs, thus expanding the area of their use.
6. Virtual nursing
The outbreak of COVID-19 served as a catalyst for many changes in the healthcare system, with virtual nurses probably being among the most prominent ones. A virtual nurse is an app with an AI bot that communicates with the patient, analyzes their medical history, and provides valuable suggestions according to their specific case. The biggest benefit of virtual nurses is their 24/7 availability and immediate response. As for their core functions, they mostly focus on remote patient monitoring, interactions with the patient, assistance in performing regular health check ups, and reminders about medication intakes or upcoming appointments.
As virtual nurses are already being integrated in healthcare facilities and telemedicine products, both doctors and patients recognize its benefits. In some healthcare facilities, the nurse turnover rate was reduced from 39% to 8.1% after the introduction of virtual nurses and the average inpatient stays were reduced by over 7%. As patients report higher satisfaction due to more personalized care, more and more healthcare facilities (52% in 2024) plan to implement virtual nurses.
7. RPA in surgery
Robotic process automation (RPA) powered by machine learning is a powerful tool that already assists surgeons in a wide range of applications. Robots can not only assist in performing a surgery but can also access specific body parts (pelvic cavity, skull base, coronary arteries, retina) with more precision than a human surgeon would. The biggest benefits of using robotic process automation services in surgery include:
- Minimized surgery time: 19.1% reduction in surgery completion time with a semi-autonomous robotic control;
- Reduced risks of blood loss: up to 50% less blood loss during robot-assisted pedicle screw insertions;
- Shorter recovery time for the patients: postoperative hospital stays among patients that underwent robotic-assisted surgery, were shorter by 2 days, compared to patients who underwent traditional surgery.
Smart care happens together: Humans + AI
While the role of machine learning in healthcare is significant, we shouldn’t forget that the technology is not perfect. Models can accurately predict which patients are at risk of getting worse or which patients will get readmitted soon, but their performance directly depends on the training data. If there is missing information, data inconsistency, or biases, the models will deliver unreliable outcomes – and it’s the responsibility of a healthcare facility to take care of its data before using it for model training.
When talking about ML implementation in healthcare, I believe it’s best to let humans and machines work together. A model can highlight high-risk patients or chances of disease development, but it’s doctors who make the final call on further treatment. And if clinicians use tools that explain the model’s reasoning, it will contribute to building trust and safety.
Real-life use cases of ML in healthcare
We’ve looked at the main ways how machine learning technology can be used in the field of healthcare – now let’s look at the real-life machine learning use cases in healthcare.
AI-powered ICU brain monitoring
The Cleveland Clinic has partnered with Piramidal Inc. (an AI startup from San Francisco) to introduce an AI model for reading brainwaves among patients in the ICU. The model is trained to read the EEG (electroencephalogram) data and analyze it in mere seconds, in contrast to hours of manual data analysis that is usually performed by healthcare professionals.
The use of this model will help doctors detect seizures, strokes, and brain injuries at the early stages, thus increasing the chances of saving patients’ lives. And considering that ICUs are often too busy and lack personnel to monitor each patient closely, the use of the AI model will greatly reduce the workload while also increasing the efficiency of patient monitoring.
An AI platform for lung cancer detection
The Red Dot system, developed by the Behold.ai company and powered by AI, is used by several NHS hospitals for analysing triage chest and CT scans. The platform can flag potential lung cancer signs within 30 seconds and reduces the patient CT wait time from weeks to minutes. The platform not only speeds up the diagnosis process but also removes 15% of the workload, according to the official website.
A smart bot for automating clinical documentation
A smart AI bot is used at Chelsea and Westminster NHS Trust and helps doctors write medical notes faster. Based on the patient’s diagnosis and results of medical tests, the bot creates a summary, which is then reviewed by medical professionals. The automation of medical documents generation leads up to faster inpatient discharge and reduces the workload for the doctors.
Main challenges of implementing machine learning in healthcare
Though machine learning is a highly beneficial asset for potential investment, its implementation also comes with certain challenges and roadblocks that one needs to be aware of in advance. Below, we list the main things to consider if you plan to introduce machine learning to your organization.
Black box problem
The black box issue aka the lack of transparency and explainability is the number one pain point of any ML-powered product. The issue becomes especially critical in healthcare, where doctors hold immense accountability for their actions and must explain them.
Solution:
- When developing an ML model, use explainable AI (XAI) tools and explanation techniques like LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), PDPs (Partial Dependence Plots) or Integrated Gradients.
- Consider using interpretable models like decision trees or logistic regressions when transparency is vital.
Data privacy and security
Patient data is highly sensitive and is protected by regulations such as GDPR and HIPAA. Hence, when introducing an ML solution, it’s paramount that this solution adheres to regulatory standards and ensures clinical data anonymization.
Solution:
- Implement data encryption, access control management, and secure storage
- Use federated learning or similar techniques so raw data is not exposed during the model training process.
- Regularly perform compliance audits
Data availability and quality
The efficiency and accuracy of an ML model depends directly on the quality and quantity of data. And while healthcare organizations normally work with massive data sets, these sets are often scattered, unorganized, biased, or noisy. The lack of data availability is usually among the biggest roadblocks that prevent a healthcare organization from the ML adoption.
Solution:
- Consider using synthetic (artificially created) data for better diversity.
- Partner with other institutions to build a shared and anonymous database.
- Invest time and resources into organizing the existing data to build reliable, unbiased, and high-quality data sets.
Legacy systems in use
When a healthcare organization plans to introduce an ML model, it needs to be ready to review the existing infrastructure and evaluate whether the model can be integrated securely and effectively. There is often a clash between advanced tools that power the ML model and outdated technologies that hospitals use. The existing legacy systems simply cannot connect with ML models and support them effectively, not to mention the lack of scalability.
Solution:
- Review your current infrastructure and try prioritizing what components should be modernized in the first place and which ones are essential for ML implementation.
- Partner with an experienced healthcare software development vendor who can help you gradually optimize your system and migrate needed components to more advanced tools and platforms.
Resistance from medical staff
One more issue that actually prevents many organizations from the ML adoption is the resistance from healthcare specialists. Some fear that AI will take their jobs and others simply don’t understand it enough to give it a try.
Solution:
- Provide comprehensive employee training that will involve not only work with the ML model but also education on its importance and use cases.
- Involve clinicians and doctors in model development and validation stages for better transparency and clarity.
ML technologies used in healthcare
When discussing machine learning in healthcare, it’s also worth mentioning the most common types of machine learning models:
Supervised machine learning
The model is trained on a dataset that has been prepared through data labeling, where every input is paired with its corresponding label before training. In this way, the algorithms “learn” on the provided information and use it to make assumptions about new inputs.
In healthcare, this model is used for medical diagnosis as it can learn on existing medical images and assist doctors in disease identification on new images.
Unsupervised learning
The model receives a dataset without any labels, independently finds patterns and structure within the given data and categorizes them correspondingly. As a result, the model groups together inputs with similar characteristics like age, date of birth, location, etc.
In healthcare, unsupervised learning usually serves research purposes and is often used in identification of new disease causes or new groups of patients. By that we mean identification of new subsets of patients that share previously unrecognized patterns (i.e., disease subtypes, similar risk factors, etc.).
Reinforcement learning
The model performs an action (i.e., adjustment of a medication dose) and receives feedback on the performance. Based on this feedback, the model will adjust its future actions to achieve the needed results and improve the performance.
One of the most popular examples of reinforcement learning in healthcare is creation of personalized treatment plans. Based on the patient’s response and the medical history, the model will be adjusting the proposed plan to fully meet the patient’s needs.
Deep learning
A subset of machine learning that operates on the base of multilayered neural networks and aims to mimic the human brain thinking. In healthcare, deep learning is especially useful in EHR (electronic health records) analysis, medical imaging, and drug discovery. The reason for that is that deep learning excels at analyzing unstructured and complex data, which is exactly what these domains are composed of.
Summing up
Today, the popularity of machine learning in healthcare is growing steadily though there are still several challenges that prevent its wider adoption. These limitations include lack of structured data, bias, lack of expertise, ethical issues, and poor adoption strategies. However, healthcare professionals and data scientists are already working on these challenges, striving to make machine learning an integral part of the healthcare industry worldwide.
It is safe to assume that in the near future we will see machine learning in more and more healthcare facilities. This also means that healthcare IT services will grow in popularity since it is impossible to build a reliable and secure ML model without the external expertise of machine learning specialists. If you have any questions regarding the development of a custom ML model or simply want to assess its profitability and learn more about machine learning in healthcare, SoftTeco will gladly answer your questions and will provide you with more information upon request.
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