AI in Medicine: Real World Examples That Are Transforming Health Care

Key takeaways

Artificial intelligence (AI) in medicine uses technologies like machine learning, deep learning, natural language processing, computer vision, and robotics to improve diagnosis, streamline workflows, and enhance patient care.

• The medical industry has rapidly increased its implementation of AI solutions in recent years, adopting it at a rate that’s more than double that of the broader economy [1].

• The health care sector utilizes numerous types of AI, including machine learning, deep learning, natural language processing, computer vision, and robotics.

• You can upskill with online courses and formal programs to keep up-to-date on the latest in medical AI and remain competitive within your field.

Explore ways in which AI in medicine is transforming patient care and optimizing workflows for busy health professionals. If you want to develop a solid foundation in AI technology, consider enrolling in the IBM Generative AI Fundamentals Specialization. 

Types of AI in medicine: Technologies transforming the medical field

AI encompasses various technologies, acting as an umbrella or nesting doll, within which you’ll find subsets that offer specialized capabilities. Within the larger field, you’ll find technologies like machine learning, deep learning, natural language processing, computer vision, and robotics, all of which are already in use in medicine. 

Although the health care industry has historically been slow to embrace change, it is adopting AI-powered tools and technologies at a rate more than twice as fast as other sectors, with an estimated 22 percent of health care organizations integrating AI into their operations in 2025, up from just 3 percent in 2023 [1]. Explore how these technologies are already transforming health care in more detail to gain a deeper understanding of AI’s potential. 

Machine learning

Machine learning is a technology that utilizes algorithms to train models, allowing them to engage in data-driven learning over time. As a result, machine learning-powered systems are more scalable and flexible, with the ability to predict accurate responses, detect patterns within data sets, and take appropriate actions based on the environment.

In modern medicine, tools that use machine learning can help support medical professionals in their daily tasks, pinpoint emerging trends, and improve organizational efficiency. Other uses include predicting diseases and powering robots that assist in some procedures. 

Deep learning

Deep learning is a subset of machine learning that helps machines learn to process data much like the human brain does. It relies on layers of interconnected nodes that work similarly to neurons, giving it powerful versatility and the ability to perform complex tasks. Deep learning is among the chief technologies that power self-driving cars. In health care, it can aid practitioners in analyzing medical images, empower clinicians to detect diseases like cancer and dementia earlier, and assist surgeons during operations. 

Natural language processing 

Natural language processing (NLP) uses machine learning and deep learning to help machines understand and communicate using human language, using computational linguistics, a practice that combines computer science and AI. It processes human input in a way that computers can understand, allowing them to perform tasks and respond in a more human-like manner. 

NLP helps health care professionals streamline documentation, automate data entry to free clinicians' time to focus more on patients, and power more efficient data analysis for decision-making. It powers tools like speech recognition programs that medical professionals can use to transcribe notes, gain valuable insights from large volumes of data for improved medical decisions, and employ chatbots and virtual assistants to enhance the patient experience.

Computer vision

Computer vision utilizes machine learning techniques to enable machines to process, analyze, and interpret visual information. It is a process that includes image recognition, where machines identify the details within an image, and reorganization, during which the machine assesses relationships between data points within the image. In today’s medical landscape, computer vision can aid radiologists in reading medical images for improved diagnostics, assist surgeons with minimally invasive procedures, and help monitor patients in real-time. 

Robotics 

Robots powered by AI often incorporate machine learning, NLP, computer vision, and edge computing. This technique offers reduced latency and real-time responses because it processes data close to its source. These robots can interact with users in real-time, operate in complex environments, and make rapid, yet adaptable, decisions. In health care settings, AI-driven robots can aid in surgeries, cleaning patient rooms, and even interact with patients and visitors. 

Learn more: AI vs. Robotics: What’s the Difference? 

How is AI used in medicine? 

One of the leading uses for AI in medicine today is disease detection and medical diagnoses. The technology offers the potential for more accurate interpretations of images. For instance, one AI tool might be able to provide precise timing for when a stroke occurred based on a brain scan, giving doctors critical information to base treatment options on. Another tool might be able to read X-rays with greater accuracy, and still another might detect early signs of disease and predict eventual diagnosis of heart or kidney disease, for example. 

Analyzing images and aiding in diagnosis is only the beginning. Examine some of the additional uses of AI in the medical field to better understand its potential. 

Streamlining workflows and administrative tasks

Recording notes, completing documentation, and reviewing medical records can take a significant amount of time for health care professionals. Medical teams can also utilize AI-powered tools to take medical histories, upload information to patient records, and send communications to patients. Using AI and machine learning to automate many of these tasks can help improve accuracy and give clinicians more time to spend focusing on patient care.

Advancing personalized and precision medicine

The insights that AI can generate and its ability to both reason and learn are driving improvements in personalized and precision medicine. These two health care approaches are intertwined, with personalized medicine customizing treatments to each patient based on factors like their lifestyle, preferences, and genetics to address all of the internal and external elements that play a role in overall health. Precision medicine goes one step further with a more robust use of data, such as biomarkers and molecular profiling. AI empowers clinicians, researchers, and data scientists to parse vast and complex sets of data for more robust analytics and interpretation, which can help deliver increasingly more personalized and effective patient care.

Accelerating drug discovery and development

The traditional approach to drug discovery and development can take significant amounts of time and money. Integrating AI into the mix can help streamline and accelerate the process. The technology supports more efficient and effective processes associated with discovering and developing new drugs, including property and toxicity assessment, target and chemical compound identification, predicting the structure of target proteins, drug monitoring, and peptide discovery and synthesis.

Enabling AI-assisted procedures and real-time guidance 

Robotics and AI-powered robots and tools are transforming the way surgeons perform procedures. Research shows that AI-powered robotic surgeries and tools that support surgeons with real-time guidance typically require less time in the operating room, with improved accuracy, enhanced recovery times, and a decrease in post-surgical complications. 

In April 2025, Proprio announced that its platform, Paradigm, which provides three-dimensional dynamic views to provide real-time support throughout procedures, received its second clearance from the US Food and Drug Administration (FDA). This allows the platform, which is the first of its kind, to measure surgeons’ progress throughout the procedure without them having to scrub out and take additional imaging, allowing for improved accuracy and outcomes [2]. 

Strengthening public health surveillance

AI empowers officials and organizations with a greater ability to collect, analyze, and interpret data that surpasses the capability of human activity alone. This allows for improved outbreak detection, contact tracing, and trend monitoring. For instance, the US Centers for Disease Control and Prevention (CDC) is already utilizing AI to support public health monitoring and initiatives. Some of the ways it uses technology include: 

• Performing faster analysis of quarterly grant reports

• Assessing satellite images to aid in detecting potential sources of Legionnaires' outbreaks

• Monitoring news reports for situational awareness and enhanced outbreak response

Enhancing access to care

The World Economic Forum estimates that 4.5 billion people across the world lack adequate access to health care and theorizes that AI could help address that concern [3]. A few of the ways that AI can help improve access to care include the following:

• Wearable technologies offer health care professionals the ability to monitor patients remotely, enhancing telepatient care and boosting patient engagement.

• Virtual health assistants can help answer patients’ questions, perform preliminary assessments, and direct patients to the medical services they need. 

• Chatbots and AI-powered apps can offer 24/7 mental health support from any location with an internet connection.

Furthermore, consider the results of one clinical study, in which researchers compared the performance of deep learning algorithms, specifically deep convolutional neural networks (CNNs), in assessing clinical images for skin cancer. In the study, the  CNN performed as well, and in some cases better than, dermatologists. The goal of the study was to evaluate the performance of the technology and determine if it could extend dermatologists' reach outside of a clinical setting. Mobile devices equipped with similar technology could potentially provide broader diagnostic care and increased access to medical professionals at lower costs [4]. 

Could AI ever replace doctors? 

AI is unlikely to replace doctors altogether due to its limitations in using judgment and applying clinical reasoning. It is, however, already transforming the field and enhancing the abilities of medical professionals, rather than replacing their need. 

Dr. Margaret Lozovatsky, vice president of digital health innovations and chief medical information officer of the American Medical Association, notes, “One of my colleagues says it best: ‘We don't believe that AI will replace physicians, but we do believe that physicians that understand how to use AI will replace those that don't’” [5]. This underscores the greater need for upskilling within the medical community to ensure professionals can use the tools that offer resources to clinicians and patients alike. 

What are real-world examples of AI in health care?

AI has the power to transform patient care and other aspects of health care operations. It’s already in use at many major medical organizations. It is also the subject of extensive research assessing its benefits and limitations, as facilities across the world consider how to best implement the technology. Check out a few well-known names in the health care industry and how they are leveraging the power of AI.

Cleveland Clinic

The Cleveland Clinic uses AI in several ways. For example, in September 2025, it announced its expanded implementation of an AI-powered clinical intelligence platform from Bayesian Health. Already in use in 13 hospitals, the organization planned to continue rolling out the platform to additional hospitals after seeing real results in identifying sepsis, a dangerous infection-related immune response that can be challenging to diagnose and is among the leading causes of in-hospital deaths. During the pilot program evaluating Bayesian Health's platform, the Cleveland Clinic found a significant decrease in false alerts and a 46 percent increase in identifying new sepsis cases and a seven-fold increase in alerts before antibiotic treatment [6]. 

The organization also began using AI Scribe in 2025. This AI-powered software from Ambience Healthcare provides ambient listening during patient visits and generates detailed reports for the patient's electronic health record [7]. 

Stanford Health Care

Stanford Health Care was among the first facilities to test a large language model (LLM) designed to respond to patients' electronic messages. The five-week study, which involved advanced practice practitioners, attending physicians, nurses, and pharmacists, revealed a significant improvement in health care professionals' task loads and work exhaustion [8]. 

Researchers also developed a new Generative AI model, SyntheMol, to help combat the problem of drug-resistant bacteria. SyntheMol helps create novel drugs, with promising initial results that suggest its potential for therapeutic use [9, 10]. 

Mass General Brigham

In 2017, before AI became the fast-moving, transformative technology it is today, Mass General Brigham agreed to a 10-year partnership with GE Healthcare Technologies, accelerating its digital solutions adoption. One of those solutions is the rollout of foundation models, which offer an adaptable foundation that providers can use for more reliable medically-focused AI application development. The goal? To increase the efficiency and accessibility of health care for the organization's patients and the communities it serves [11]. 

In December 2025, it announced the launch of AIwithCare, an AI company developed by Mass General researchers to expedite the process of finding clinical trial patients. The screening tool RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review (RECTIFIER) uses generative AI to assess patient health records and assess them for potential eligibility in clinical trials [12]. Early research reveals RECTIFIER is more accurate in identifying eligible patients compared to traditional, manual screening. Enrollment rates nearly doubled [13].

Mayo Clinic

At the Mayo Clinic, AI is helping providers treat strokes and detect heart disease faster. In a study, the organization used an AI-powered screening tool to predict patients at risk of ventricular dysfunction, a heart problem with no clear symptoms, 93 percent of the time. The Mayo Clinic also developed AI to use in tandem with Apple Watches to identify patients with low ventricular ejection fraction, a condition marked by your heart failing to pump blood normally [14].

What are the advantages and challenges of using AI in medicine?

AI holds significant promise in the health care arena, but you also need to be aware of some concerns.

For example, a study published in the New England Journal of Medicine assessed AI's capabilities in identifying patients whom hospitals can safely discharge after surgery. The results revealed the model's ability to provide real-time predictions in 86 percent of cases. However, it also revealed its dependence on data quality and governance for the best results, underscoring the need for professionals to oversee the tools [15]. 

With that in mind, consider the following benefits and drawbacks associated with implementing AI in health care. 

Advantages

Implementing AI can help health care providers provide more informed patient care. AI and machine learning models support medical professionals in gaining real-time, evidence-based treatment recommendations and insights for improved care decisions. Other benefits include the following:

• Fewer medical errors

• Better patient outcomes

• Reduce the cost of patient care

• Improve ability to provide personalized care

• Boost research efforts to understand diseases and identify biomarkers easier

• Shorten timeframes and reduce expenses associated with drug research and development

Potential limitations

One of the most compelling limitations of AI in the medical field is the ethical challenge it poses. Keeping patients' data protected and their privacy safeguarded according to regulations is essential. 

Perhaps even more significant is the potential for algorithmic bias, which can occur when AI trains using data sets that lack an accurate representation of the population. Many communities are historically underrepresented, which could lead to misdiagnoses and further exacerbate existing problems with equality.

Additional drawbacks to consider include the following:

• Implementation costs can be high because AI requires ongoing maintenance as well as training for successful adoption

• Incomplete, inconsistent, and low-quality data can erode accuracy and call results into question

• Human oversight and collaboration are necessary to leverage AI in the most responsible, effective ways

What is a real-world example of AI in health care? 

Microsoft released Dragon Copilot, an AI assistant for health care clinicians, on May 1, 2025, in the United States, with a plan to roll it out in Canada, the United Kingdom, France, the Netherlands, and Germany over several months [16]. Dragon Copilot offers an excellent example of AI's use in health care, as it helps optimize workflows for doctors, nurses, and other members of the health care team. 

In addition to capturing conversations in multiple languages, both during care and recordings, that the program converts into customizable notes for streamlined documentation, Dragon Copilot also automates tasks, including after-visit summaries, referral letters, and making coding suggestions. Microsoft also notes that Dragon Copilot offers a combination of safety, security, privacy, and trust for health care-related AI that's responsible and trustworthy for use in such a sensitive setting, with features that free clinicians’ time for a more focused attention on patients.

Explore our free resources to continue learning about AI 

Whether you’re exploring a new career path or learning about new and emerging technologies, our free weekly LinkedIn newsletter, Career Chat, can help you discover valuable information and stay updated on all the latest. You might also check out the following AI resources:

• Watch on YouTube: How to Prepare for AI in Your Career | Expert Tips from Tech Leaders

• Learn from an expert: The AI Advantage: 9 Questions with UC Davis AI Instructor Sadie St. Lawrence

• Bookmark for later: Artificial Intelligence Glossary: Learn AI Vocabulary 

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