As one physician notes on Sermo, technologies like machine learning and artificial intelligence have “great potential but [are] a little scary.”
Many physicians agree with this feeling. While machine learning has facilitated notable clinical and institutional advancements, how it will continue to change medicine remains to be seen. However, when Sermo asked physicians in the U.S. what the role of AI and machine learning in diagnostics would be over the next five years, the majority (57%) said it would become a routine part of practice across many specialties.
This guide explores the current landscape of machine learning in healthcare, including its benefits and what these tools mean for your clinical practice.
What is machine learning in healthcare?
Machine learning is a subcategory of artificial intelligence where computer systems are trained on patterns of massive data in order to make predictions without being specifically programmed for each task. In healthcare, this means analyzing thousands of clinical data reports like imaging, lab values, patient records, and genomic sequences to support diagnosis, predict outcomes, or optimize workflows.
Machine learning models fall into four broad categories: supervised, unsupervised, reinforcement, and semi-supervised learning. Here is how each model applies to medicine:
Supervised learning
Supervised learning models are trained on labeled data to predict known categories. A clinical example is detecting breast cancer on mammograms. Clinicians train a supervised machine learning model on thousands of labeled mammogram images so the system learns to recognize subtle patterns associated with malignancy by comparing its predictions with radiologist-confirmed diagnoses.
Unsupervised learning
Unsupervised machine learning systems process unlabeled data to identify patterns and organize information. A clinical example is clustering anonymized patient health records to reveal hidden subgroups with shared symptom patterns or treatment responses.
Reinforcement learning
Reinforcement learning is literally reinforcing the AI based on preset metrics or rules to steer its capabilities. It’s essentially learning through trial and error to optimize a strategy. A clinical example is optimizing dynamic treatment regimens, such as sepsis management protocols or insulin dosing, in simulated patient environments.
Semi-supervised learning
Semi-supervised learning models combine a small labeled dataset with a larger unlabeled dataset. A clinical example involves rare disease research. This approach is highly useful when expert-labeled medical data is scarce but raw clinical data is abundant, allowing the labeled data to guide the model’s learning efficiently.
Machine learning vs. AI vs. deep learning: what physicians need to know
Artificial intelligence (AI) encompasses a wide range of technologies designed to replicate human cognitive functions, such as learning, reasoning, and problem-solving. Within this expansive field, machine learning stands out as a specialized branch focused on developing data-driven algorithms.
These algorithms enable systems to learn from past experiences and improve their performance over time without explicit programming.
Diving further, we arrive at deep learning, a more intricate subset of machine learning that employs artificial neural networks. These networks consist of multiple layers that allow the system to process vast amounts of multi-modal unstructured data—such as images, audio, and text—efficiently and effectively. By leveraging the complexities of these layered networks, deep learning can uncover patterns and insights that might be missed by traditional machine learning methods, making it a powerful tool in the realm of AI.
As a physician judging whether or not an AI tool will be effective for your daily practice, these distinctions make all the difference. When a vendor claims their tool uses “AI,” that terminology is often too vague to evaluate clinically. When a study specifically mentions “deep learning,” the methodology is more precise, often indicating the use of convolutional neural networks to analyze complex image data. Understanding the difference between machine learning vs AI in medicine gives physicians enough literacy to distinguish meaningful clinical claims from marketing language.
To better understand these foundational terms, read Sermo’s guide on the role of AI in healthcare.
How physicians are using machine learning today
Physicians are already actively integrating machine learning tools into their daily routines. A recent Sermo survey of over 100 physicians revealed exactly how these tools show up in clinical practice.
– 60% of physicians use large language models (LLMs) to check for drug interactions.
– Over 50% utilize LLMs for diagnostic support.
– Nearly 50% use LLMs to draft clinical documentation or treatment plans.
– 70% leverage LLMs for patient education and literature searches.
Dr. Sara Farag, a gynecologic surgeon and Sermo medical advisory board member, notes: “These findings are unsurprising. As workloads grow and information volumes expand, LLMs prove increasingly valuable for physicians.”
Adoption rates vary by specialty. Many diagnostic-heavy specialties, such as radiology, lead the way with imaging AI, oncology utilizes ML for genomics and treatment matching, and pathology relies on histological analysis models. Physicians are using both general-purpose LLMs like ChatGPT and purpose-built clinical tools like Aidoc, Viz.ai, and ambient scribes like Heidi AI. A notable gap exists between personal use and institutional policy, as many clinicians across specialties experiment with ML tools before their hospitals establish formal governance.
Do industry leaders recognize the benefits of machine learning in healthcare?
Sermo surveyed over 100 U.S. healthcare decision-makers to understand institutional perspectives. 91% agree that machine learning will be foundational within five years, though just 33% anticipate shorter-term advantages. On broader current adoption trends, 45% of executives actively follow ML industry trends while only 25% have adopted these systems. Much of the same group of nonadopters (21%) acknowledge missed advancement opportunities for their institution.
Key applications of machine learning in healthcare
Machine learning healthcare applications range from diagnostics and treatment planning to hospital operations. Here are the most prominent machine learning healthcare examples currently impacting the medical field.
Medical imaging diagnosis
Deep learning medical imaging models analyze scans to detect pathologies. These models have viable dermatological, neurological, and oncological use cases. In some controlled settings, machine learning models can even match the diagnostic performance of specialists.
Building on this idea, a physician shares on Sermo: “Artificial intelligence (AI) and machine learning enable digital twins to learn from large datasets, refine predictions and provide recommendations. For example, machine learning algorithms can analyze tumor heterogeneity and predict therapy resistance… As technology advances and barriers are addressed, digital twins will likely become integral to modern oncology practice.”
Explore further reading on how artificial intelligence in radiology is transforming diagnosis.
Personalized medicine
Machine learning is central to the advancement of personalized medicine. The expansion of precision medicine parallels the growing sophistication of machine learning models, specifically in pharmacogenomics, where algorithms help match drug selection to a patient’s genetic profile and predict treatment responses.
A Stomatologist spoke to machine learning’s role in personalized medicine on Sermo, “This trend that started years ago has been increased by the fact that by 2025, AI systems are expected to be able to respond independently to specific questions from patients, especially after the health crisis. In this way, health can evolve into a completely personalized management.”
Predictive analytics and early warning systems
Predictive analytics represent one of the most widely adopted ML applications in hospitals today. Currently, 72% of healthcare organizations use AI and machine learning to analyze medical data, aligning with the Sermo community’s belief that predictive analytics will become routine standard care.
AI models are currently predicting sepsis, cardiac arrest, readmission risk, and patient deterioration before clinical symptoms appear. For example, Epic’s sepsis prediction module is deployed across major health systems to monitor patient vitals in real time.
Drug discovery and development
Machine learning accelerates target identification, molecular screening, toxicity prediction, and clinical trial participant matching. ML models can screen millions of compounds computationally, significantly reducing the timeline from initial discovery to candidate selection.
These algorithms analyze biological datasets to predict how different molecules will interact with disease targets. For more information, explore our article on AI in Drug Development.
Natural language processing in clinical documentation
Natural language processing (NLP) is the ML application most likely to directly affect your daily workflow. Our Sermo survey shows that 70% of physicians use LLMs for patient education and literature searches.
Ambient listening scribes, such as Sunoh.ai, automatically transcribe and generate clinical notes during patient encounters. Other ML in clinical practice applications include automated coding and billing systems, clinical note summarization, and advanced literature search tools.
Administrative workflow automation
Machine learning offers various use cases in clinical administration, including billing automation, claims processing, appointment scheduling, and record management. These applications drive cost savings, reduce human error, minimize manual labor, and streamline operations.
A physician posted on Sermo explaining, “[Machine learning] certainly is gaining momentum in healthcare where it is being used for research not only to make discoveries but also to help recruit and enroll clinical trial participants; for practices to automate administrative and operational tasks, to make predictions, to support patient care and diagnoses, to provide personalized treatment; and more.”
Machine learning and clinical outcomes: what the evidence shows
Evaluating how machine learning impacts patient outcomes requires looking at both controlled research settings and real-world clinical applications.
Diagnostic accuracy
Machine learning models have shown remarkable performance in various specialized diagnostic tasks within the medical field. For instance, algorithms have proven particularly effective in dermatology for the detection of melanoma, in radiology for identifying lung nodules, and in pathology for histological grading. It is important to note that these impressive results are primarily derived from controlled environments using meticulously curated datasets.
Predictive performance
Early warning AI systems can reduce mortality in sepsis and adverse cardiac events by preemptively alerting healthcare teams. Readmission prediction models can enable targeted discharge planning and optimized resource allocation. A growing body of real-world evidence published in Cureus supports these predictive AI models when deployed carefully alongside existing clinical protocols.
The research-to-deployment gap
Many ML models perform well in research settings but face significant performance degradation in the clinic. This happens due to a dataset shift, where the curated training data does not match the deployment population. Challenges with medical teams integrating AI with their current workflows, combined with already high levels of system-wide burnout, also reduce the practicality of AI deployment.
Physicians are highly aware of this limitation. According to a Sermo member survey sent to physicians in the U.S., 71% of physician respondents say the risk of misdiagnosis due to over-reliance on AI is the biggest limitation of these technologies.
Challenges of machine learning in healthcare
The challenges of machine learning in healthcare span technical data issues, ethical concerns, and practical integration hurdles.
Data heterogeneity, quantity and quality
Machine learning algorithms thrive on having access to large, high-quality, and standardized datasets. When the data is heterogeneous, biased, or incomplete, it can seriously affect the accuracy of machine learning predictions, making them less trustworthy.
This happens for several reasons. For instance, variations in how electronic health records (EHRs) are documented can create inconsistencies. Additionally, sometimes data is just missing due to various mechanisms, and diseases may not always be accurately labeled. All of these factors can undermine the effectiveness of machine learning in making reliable predictions.
Reflecting this, a physician posted on Sermo saying: “Machine learning could help identify people at risk of adverse outcomes from being prescribed opioids, new research suggests. Like all AI, the more data inputted representing specific populations and regular retraining improves prediction performance over time, leading to more effective opioid stewardship.”
Algorithmic bias and health equity
ML models trained on non-representative datasets can perpetuate or amplify existing healthcare disparities. Documented examples include dermatology models demonstrating lower accuracy on darker skin tones and risk prediction models systematically underestimating disease severity in black patients. Physicians interpreting ML outputs at the point of care need to understand these biases and adapt accordingly.
The black box problem and clinical trust
Deep learning models cannot explicitly explain their reasoning, creating the “black box” problem. This is a fundamental barrier to clinical adoption because physicians need to understand why a model makes a recommendation to trust and act on it. Explainable AI (XAI) is an emerging area of development designed to solve this.
One U.S. neurologist on Sermo noted: “Many people who have been experimenting with ChatGPT, for example, have found it has a tendency to ‘hallucinate’ or just flat make up answers when it is uncertain… I don’t think it will be useful to have AI ‘hallucinate’ answers when uncertain in medical applications. Also, who is liable when AI gets something wrong?
A geriatrician writes “… it is important to ensure that it is used ethically and responsibly, and that the privacy and security of patient data is taken into account.” All of this leads to many physicians debating on Sermo, “Are AI tools making doctors worse at their jobs?”
Regulatory and liability landscape
As of 2026, there are over 500 FDA-cleared AI and machine learning medical devices, with the majority functioning in radiology. The regulatory framework for algorithm updates and continuous retraining is still maturing. A major unresolved liability question remains: when an ML model contributes to a diagnostic error, who is responsible?
Integration and interoperability
Medical institutions cite the risk of predictive errors, privacy, security, and complexity as primary adoption concerns. Tools need to integrate seamlessly into EHR and PACS workflows using standard FHIR interoperability protocols without disrupting established clinical processes. According to Sermo survey data, about 40% of respondents said inadequate integration was a major risk factor when using AI in diagnostic medicine.
What physicians should know going forward
Machine learning requires physicians to begin adapting their approach to adopting new medical tools.
ML literacy as professional competency
Physicians do not need to code, but they need to be able to evaluate ML tool claims critically. Understanding training data sources, validation methods, and known limitations is becoming as important as understanding a drug’s mechanism of action before prescribing it. Will AI replace doctors in the future? No, but physicians who use ML will have the advantage over those who do not.
Evaluating ML tools
When evaluating AI and machine learning in medicine, physicians should look for FDA clearance status, validation on diverse patient populations, explainability of outputs, and integration with existing workflows. Monitoring the ML tool after deployment with feedback loops is essential to ensure the tool maintains accuracy over time.
Physician involvement in governance
ML tool selection, deployment policies, and quality monitoring should include direct input from physicians. Without clinical oversight, institutions risk deploying tools that optimize for operational metrics that do not align with actual patient outcomes.
Frequently Asked Questions
What is the difference between AI and machine learning in healthcare?
Artificial intelligence is a broad concept referring to systems that simulate human intelligence. Machine learning is a subcategory of AI that uses algorithms to learn patterns from historical clinical data to make predictions or decisions without specific programming.
How does machine learning improve patient outcomes?
Machine learning improves patient outcomes by powering early warning systems that detect conditions like sepsis before clinical signs appear. It also enhances diagnostic accuracy in medical imaging and helps match patients with targeted therapies based on their genetic profiles.
What are the biggest risks of using machine learning in medicine?
The biggest risks include algorithmic bias that can worsen health disparities, dataset shifts that degrade model accuracy in real-world settings, and the “black box” problem where algorithms cannot explain their clinical recommendations to the prescribing physician.
Join the conversation on Sermo
Machine learning in healthcare is becoming the norm rather than the exception. AI is transforming clinical workflows, from imaging triage to documentation scribes to predictive analytics. Physicians who engage with these tools critically and participate in their governance are better positioned to ensure ML serves clinical quality rather than just operational efficiency.
On Sermo, physicians discuss policy, patient concerns, and the effectiveness of AI, machine learning, and other breakthrough medical technologies.
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