13 Examples of AI in Healthcare [2025]

Artificial intelligence (AI) is quickly becoming a mainstay across industries. With computers now capable of performing cognitive-level actions without human input, their potential is beginning to be understood and leveraged for a wide range of exciting purposes. 

Big data adoption is further enabling AI, acting as fuel to inform its fundamental reasoning and understanding. Where humans struggle to spot patterns hidden within mountains of data, AI can do so quickly and with incredible accuracy. 

Unsurprisingly, AI in healthcare is expanding quickly, with new and novel ways to transform modern medical practices being discovered daily. 

This article will explore thirteen examples of AI in healthcare. We’ll explore how this technology innovates outdated practices and upends industry norms.

AI in healthcare examples at a glance:

AI Technology	Function	Category
AI-driven diagnostics	AI learns from medical information, such as scans and health records, to help find diseases earlier and more accurately.	AI-driven diagnostics
Preventive screening algorithms	Software analyzes test results and habits to identify hidden health risks before they become significant problems.	AI-driven diagnostics
Predictive risk modeling	AI analyzes your health background to predict if a sickness might worsen or if a medical emergency could occur.	AI-driven diagnostics
Tailored treatment plans	Smart programs design treatments just for you, based on your health story and what’s likely to work best.	Treatment & personalisation
Oncology-specific personalization	For cancer, AI selects treatments that target the specific type of cancer cells present in your body.	Treatment & personalisation
Surgical robotics and guidance	Robots, guided by AI and your body’s details, help surgeons operate with super accuracy and fewer mistakes.	Treatment & personalisation
Accelerated drug development	AI invents new medicines on computers and figures out how they might work in the body, speeding up discoveries.	Treatment & personalisation
AI in clinical education	Doctors and nurses practice diagnosing illnesses on realistic computer games that change based on their skills.	Clinical operations & medical training
Streamlined clinical trial design	AI quickly finds the right patients for studies on new medicines, making the tests faster and more dependable.	Clinical operations & medical training
Automated medical text analysis	Computers read doctors’ notes and pull out important facts, organizing medical information much faster.	Clinical operations & medical training
Automated patient care	Gadgets you wear and smart chat programs handle simple health tasks and watch your body’s signals as they happen.	Patient support & population health
AI mental health tools	Apps and chatbots offer mental health support and track your well-being, anytime you need it.	Patient support & population health
Genetic pattern recognition	AI examines your genes to identify potential clues about diseases you may develop, helping to plan ways to maintain your health.	Patient support & population health

AI-driven diagnostics

AI alters how doctors diagnose diseases, offering faster and more precise insights. The ability to detect conditions earlier helps save lives and reduce costly delays in treatment.

AI-driven diagnostics

Healthcare professionals utilize clinical decision support systems (CDSS) to aid in making critical medical decisions for their patients. These systems provide a central place for doctors to access patient data, reports, guidelines, and other relevant information.

CDSS is also key for delivering diagnostic support. AI enhances doctors’ ability to diagnose medical conditions with unmatched speed and accuracy.

A study by The EveryLife Foundation for Rare Diseases estimates that $86,000 to $517,000 in avoidable costs per patient can be attributed to late diagnosis. Worrying symptoms in patients can often go undetected, leading to unnecessary delays in receiving the right treatment. 

AI can cleverly spot seemingly benign conditions in CT scans, with applications in lung cancer detection and diagnosis already underway. Reports found that an AI outperformed the Brock model’s ability to differentiate between cancerous and non-cancerous lung nodules, which are common in roughly one in every three people.

Preventative screening algorithms

Preventive screening algorithms, a linchpin of AI-driven diagnostics, are quietly reshaping healthcare. 

Instead of waiting for symptoms, they scan patient data, including laboratory results, imaging, genetics, and lifestyle factors, for early signs of disease. These models detect subtle patterns that clinicians might miss, flagging risks for conditions such as cancer, diabetes, or heart disease long before clinical onset. 

The goal isn’t to replace human judgment but to surface possibilities earlier and more accurately. As they learn and adapt, these algorithms enable targeted interventions, shifting care upstream. The results show improved patient outcomes, decreased costs, and a shorter time to transforming into a clinical asset.

Predictive risk modelling

Within the CDSS category, AI-led predictive risk modeling is gaining momentum in healthcare.

Clinical decisions can have serious consequences if the information used is incomplete or inaccurate. Decision support must be thorough, using the most recent data to provide an accurate and current view of a patient’s condition.

AI helps improve decision support systems (DSS) by predicting outcomes, potential diseases, and future care needs, as well as creating risk models. It detects patterns by analyzing various medical data, such as past illnesses, genetic factors, and blood test results, to predict how conditions may worsen.

Risk models are being developed to identify hidden trends suggesting a future medical event or emergency. These advances enable the monitoring of conditions in patients with diabetes, kidney disease, neurodivergent conditions, cancer, and COVID-19.

Treatment & personalisation

Advancements in treatment and personalization are enabling healthcare to become more tailored and precise. 

With improved data analysis, healthcare providers can now deliver customized therapies tailored to each patient’s specific needs, enhancing both effectiveness and outcomes.

Tailored treatment plans

Healthcare is arguably one of the most consequential industries in the world, with every decision potentially spelling the difference between life and death.

As such, the pressure faced by modern medical professionals to deliver industry-leading standards is increasing, with projections from the NCHWA, via the Health Resources and Services Administration, estimating a shortfall of 187,130 full-time physicians by 2037. 

As we know, AI is helping to alleviate some of this burden across many healthcare segments. Tailoring patient treatment plans is one area where it excels. What would require hours of gleaning manual paperwork, cross-referencing reports and documents to create a tailored treatment plan can now be done with AI.

Data once again sits at the crux of this, feeding patient data into machine learning models (ML) and deep learning (DL) algorithms that use it to create a holistic understanding of the patient. It can then weigh past and current medical data with future predictions to develop treatments tailored to an individual’s needs.

Oncology-specific personalization

One-size-fits-all health procedures often give the same treatment to all patients with the same condition, missing the chance for more personalized care.

AI is helping make cancer care more personal with a multi-step approach. Companies like Tempus, NVIDIA, and IBM offer software focused on cancer. It can detect molecular changes from different data sources to predict how tumors will grow. 

AI also examines tissue changes, metabolism, and proteins to create a more personalized model. With greater accuracy, AI helps doctors categorize cancers, such as lung and breast cancer, into more specific and detailed types.

The AI in oncology market is expected to grow from USD 2.45 billion in 2024 to USD 11.52 billion by 2030. Oncologists can better respond to patient needs by matching therapies to the actual drivers of disease, rather than just its location or stage. 

Surgical robotics and guidance

Personalized medicine is transforming surgery with advancements in genomics, AI, and robotics. They assist surgeons with greater precision, using real-time data and patient-specific models to guide movements and avoid vital structures. 

AI plans surgical paths, adjusts to anatomy, and learns from past procedures to improve outcomes. This leads to fewer errors, quicker recovery, and more consistent results. These systems don’t replace human skill but work harmoniously to enhance it. 

Each procedure becomes more tailored, with decisions informed by thousands of previous cases and the patient’s data. The focus shifts from general technique to individual need. 

Accelerated drug development

The final category explores examples of AI technologies fast-tracking pharmaceutical discovery and development. 

For example, advanced AI can help create brand-new drug candidates. Instead of testing thousands of random chemicals, their AI tools learn what a successful drug looks like, what it needs to work well in the body, and avoid side effects.

Once the AI understands these patterns, it can design new molecules that are more likely to be safe and effective. However, before testing on people or animals, AI can simulate how a drug might act in the body, checking for safety, stability, and unintended side effects. 

AI can also scan vast amounts of biological data to identify body parts (e.g., proteins or genes) associated with diseases. Companies use this to reveal overlooked treatment targets, making it easier to develop new types of medicine.

Clinical operations & medical training

AI is streamlining clinical operations by reducing administrative burdens and enhancing patient flow, enabling healthcare workers to focus on patient care. 

In medical training, AI-powered simulations and AI tools for data analysis offer hands-on experiences, enabling trainees to sharpen their skills in a controlled, real-time environment.

AI in clinical education

Clinical education is becoming increasingly dynamic and precise with each passing day. Smart simulations now mimic complex cases, allowing trainees to practice safely. Virtual patients can even respond in real-time, helping learners sharpen their diagnostic skills. 

Performance is tracked, feedback is instant, and training adapts on the spot. In operations, intelligent tools help manage schedules, monitor patient flow, and reduce admin strain. 

Learning and practice are no longer separate. This is because data from day-to-day care feeds back into training. Medical professionals stay sharp, systems run better, and the focus remains where it matters most.

Streamlined clinical trial design

AI is also helping to optimize clinical trial design more strategically. 

Traditional, long-standing trial setups can be lengthy, expensive, lack relevant data, and require intensive input from medical professionals. AI business models, however, can refine the clinical trial process to yield more targeted and statistically sound outcomes.

One example is AI’s ability to identify patients most likely to respond to a treatment. It scans large datasets to flag specific traits or risk factors, including medical histories, lab results, and genomic profiles. This refined patient matching accelerates recruitment and enhances the reliability of trial outcomes.

Automated medical text analysis

Bulky, siloed, and disparate medical data are prevalent across many healthcare organizations. Historic documentation, patient records, and digital copies of paper documents on legacy systems remain underutilized. 

AI-automated medical text analysis is addressing this issue with machine learning (ML) clinical language models and natural language processing (NLP), which are now capable of collecting, interpreting, and processing medical text from various unstructured data sources. It can differentiate between symptoms, diagnoses, medications, procedures, and glean family medical history, among other things. 

This is augmenting medical workflows across the field with solutions that deliver clear and actionable data, enabling improvements to knowledge bases and clinical decision support systems (CDSS) output. 

Patient support & population health

AI in healthcare enables a more proactive approach to patient care, offering tailored support that adapts to individual needs. It equips public health systems with powerful tools to monitor and address emerging trends, improving outcomes across entire populations.

Automated patient care

AI-automation is alleviating the pressures of day-to-day patient care for healthcare workers. 

These systems handle various practical tasks, including scheduling appointments and sending follow-up reminders. They also summarise audio transcripts and approve repeat prescriptions.

The CDC reports that nearly 46% of healthcare workers reported frequent burnout in 2022, an increase from 32% in 2018. Those with sufficient time to complete their tasks reported fewer mental health challenges.

AI-automated patient care extends to patient monitoring, where AI-enabled wearables equipped with sensors (e.g., an Apple Watch or health rings) monitor and track patient information in real-time. These gadgets monitor heart rate, vital signs, body movement, and blood sugar levels, among other things, to create a live view of patient status. 

Elsewhere, AI healthcare chatbots, agents, and virtual assistants are standardizing round-the-clock patient care with 24/7 available chatbots that can answer patient needs at a moment’s notice. 

These solutions aid with medication reminders, symptom check-ins, and post-surgical instructions, and so lower demand on healthcare workers and the need for in-person appointments.

AI mental health tools

AI-driven mental health tools offer real-time support by tracking behaviors and emotions. These tools don’t replace therapists, but they help by identifying signs of distress that might be missed. 

Examining aspects such as speech, activity levels, and social interactions provides doctors with more detailed insights into a patient’s mental health. Quick feedback helps identify problems early, enabling faster and more cost-effective assistance. 

AI tools also make mental health care more accessible, especially in areas with limited resources, making it easier for the average person to receive the support they need.

Genetic pattern recognition 

Genetic pattern recognition is elevating precision medicine to new heights with the aid of AI. Machine learning models utilize genomic data as part of their training to comprehend the functioning of human DNA. 

These models can learn the structure of specific gene panels and even understand more sophisticated whole-genome sequences. This can create a genetic profile, wherein risks and responses can be estimated based on how diseases interact with DNA mutations.

Once trained, a patient’s DNA can be quickly assessed for numerous genetic variants that may indicate underlying or potential health issues. AI can also target patient care in response to predictions and plan the most targeted outcomes. 

What does the future of AI in healthcare look like? 

AI in healthcare is being approached with a growing sense of urgency, and for good reason. 

It’s one of the few industries that hold the world together, intertwined with public health, human rights, and layers of legislation. 

As pressure builds and funding accelerates, it’s set to become one of the most heavily invested sectors in the AI space. 

This means a responsible and meticulous approach is necessary to deliver reliable outcomes for both patients and medical professionals. 

A steep learning curve lies ahead, and the future depends on those willing to discover, build, and make smarter choices powered by data and intelligent information.

People Also Ask

• Is AI replacing doctors?
  AI isnt replacing doctors. It helps them by analyzing medical data and providing advice promptly. However, doctors still need to make final decisions and provide care, using their experience and understanding of patients.
• How is patient privacy protected when using AI?
  AI protects patient privacy by keeping information safe through encryption. Personal details are kept secret or hidden. Only doctors and authorized staff can see it. Laws like HIPAA ensure your privacy when AI is used in healthcare.
• What are the risks of using AI in healthcare?
  AI can sometimes make mistakes if it has incorrect or incomplete information, which could impact diagnoses. Relying too much on AI may also lead to missing important details that doctors would catch. Humans need to keep checking AI’s work.
• What is AI in healthcare?
  AI utilizes technology to enhance doctors and nurses understanding of patient data. It helps identify problems, predict conditions, and recommend treatments more quickly. AI enhances healthcare accuracy, but doctors still utilize their expertise to make the final decisions.