The application of AI in healthcare and life sciences has been transformative. It has enabled clinicians to examine complex data points, at speed, in real time, leading to greater insights—and, in some cases, early detection of diseases.

According to Precedence Research, AI in healthcare will be worth around $195 billion by 2034 in the USA alone. As chronic illnesses rise, populations age, and budgets tighten, the healthcare industry—including hospitals, care facilities, pharmaceuticals, health insurers, medical device providers, and biotech companies—are increasingly turning to AI to streamline operations and enhance patient care.

So how exactly is AI used in healthcare? And what is its role in future advancements?

How is AI used in healthcare?

This is not a theoretical, in-the-future scenario; AI is already supporting healthcare practitioners in various sectors. A 2024 McKinsey survey found that 29 out of 100 healthcare leaders in the USA had already implemented generative AI tools to bolster their operations, with a further 43 seriously considering it.

AI is already making an impact in two core areas. First, it supports doctors and medical staff in their daily work—for example, by assisting in treatment planning, and automating tasks like patient monitoring and other routine administrative operations. Second, AI plays a broader role in other areas of healthcare, such as diagnostics, medical research, and enhanced disease detection.

How is AI helping doctors and other healthcare professionals day-to-day?

Doctors and nurses can have incredibly taxing and stressful workloads. This increases the risk of human error and, at times, can result in lasting burnout for practitioners. AI-powered healthcare solutions are already helping to ease the pressure in a range of ways.

Summarizing doctor/patient exchanges

Ambient listening involves having a multilingual AI tool listening in on doctor/patient interactions and assisting in the process of fully documenting key information.

A study published in the New England Journal of Medicine involved AI scribes aiding more than 300k patient interactions over the course of 10 weeks. It found a "statistically significant reduction in time in notes per appointment," with positive feedback from physicians and patients alike. If medical professionals have this technology to support them, they can typically focus more on listening carefully and effectively diagnosing and treating their patients.

Streamlining arduous paperwork

A study quoted by AVIA implemented an AI tool for analyzing patient data in 33 acute care hospitals in six states. It found that clinicians saved an average of 10 minutes per patient, per day on follow-up documentation.

Couple this with the possible impact of ambient listening tools on appointments, and you could have a major reduction in the amount of time that doctors and nurses must spend on administration, allowing for better use of highly-skilled clinical resources.

Flagging high-risk situations

Erik Swanson, senior vice president of data and analytics at healthcare management consultants Kaufman Hall, discussed some possible trends for AI and healthcare operations in 2025.

He pointed out the potential value of AI for categorizing patients based on their health risks in clinical use—known as risk stratification—as AI can more quickly recognize patients at risk of certain conditions. There are a number of tools already developed in this area, including Swaasa, a tool trained on Google’s HeAR model, that assesses lung health by analyzing cough sounds.

Assisting in student doctor training

Generative AI systems trained on or linked to medical knowledge bases can help budding doctors test their knowledge in custom ways—expediting the processes of studying for exams and engaging with challenging disciplines.

There’s potential for them to do even more, too. Stacey Caywood, CEO of Wolters Kluwer Health, predicts that 2025 could see AI assistants “transform virtual reality training by providing lifelike conversations with virtual patients”. This may help medical students accelerate their patient care skills, making them readily equipped to tackle the challenges of real-life practice once they're qualified.

Artificial intelligence in the wider medical field

Diagnostics and research generate vast amounts of data. AI's ability to analyze at scale helps uncover hidden patterns and quickly simulate scenarios, accelerating breakthroughs and testing potential treatments.

Assisting in driving research breakthroughs

A study published in Nature outlines the creation of a platform called CODA (Computational Optimization of DNA Activity) by researchers from several leading medical institutions. CODA was trained using a deep-learning model and given sequence-generation algorithms. This training made it capable of designing synthetic cis-regulatory elements (CREs), with CREs being able to control how genes are expressed.

Results have been promising, with synthesized CREs managing to isolate gene expressions to specific areas. This encapsulates what generative AI can do for medical research. By sifting through incredible quantities of data, it can figure out how to achieve highly specific outcomes.

Helping improve public health initiatives

Public health organizations can use AI to take on large-scale challenges, such as disease outbreaks. For example, a study published in Infectious Medicine looks into how AI tools were deployed to address the impact of the COVID-19 pandemic. It finds that, among other things, AI helped predict disease progression, hospitalization time, and mortality risk.

AI tools can also potentially do remarkable things with surveillance footage and social media data by looking for signs of infection, alerting on disease outbreaks, and aiding the distribution of vital resources.

Optimizing clinical trials

Running effective and efficient clinical trials is often a challenge. Researchers must sign up the most suitable patients and gather granular metrics to accurately gauge the results.

AI tools can help with clinical trials in a range of ways, from sorting through patient data to pick out ideal candidates, to drawing real-time insights from wearable devices and catching any developments that may require emergency treatment.

Benefits of artificial intelligence in healthcare

With recent technological advancements in AI, there are some notable advantages the technology can offer within the healthcare and life sciences sector.

It can improve productivity and efficiency

Medical professionals are often tasked with doing a wide range of things every day, including some that are purely administrative. Those bureaucratic demands can sometimes prevent them from using their skills to maximum effect.

With automated AI systems in place to handle rote tasks that can turn into timesinks, doctors, nurses, and researchers can focus on the things they are trained for.

It can boost accuracy and reduce errors

AI tools are developed to identify patterns and anomalies in data, making them useful for minimizing the risk of human error leading to inaccurate diagnoses. With medical professionals working in tandem with AI applications, it could lead to safer procedures and better outcomes.

It can enhance patient experiences

Generative AI systems linked with patient records can assist in offering the best possible patient experiences. They could be used, for example, to deliver personalized communication, help patients log their symptoms, or to take notes during appointments.

It can enable better resource allocation

It takes a huge amount of effort to run a hospital or medical facility, especially when resources are stretched thin—as they so often are. Through careful analysis of patient data and administrative workflows, AI systems can make optimization suggestions that may lead to greater time, cost, and staff efficiencies.

What is the future of AI in healthcare?

In the near future, it’s possible that we’ll see AI systems deployed widely and integrated seamlessly into core healthcare processes. The amount of data and insight on offer may take personalized care to the next level, using relevant factors (such as genetic, environmental, or historical) to diagnose more accurately and find better courses of treatment.

With more people wearing smart devices equipped with varied sensors, we may also see an influx of consumer-level health services. These could use AI learning to diagnose basic issues and make preventive care suggestions, reducing the pressure on hospitals. Privacy is a potential concern, particularly since AI is changing the security space, but it’s likely manageable in light of the potential advantages.

AI in healthcare is harder to predict beyond that, there are countless potential applications of AI—and healthcare companies are watching closely. If development is consistent, the use of artificial intelligence in healthcare may come to involve everything from personalized gene therapies to virtual doctors. For now, though, we can only embrace this industry and see how it develops.

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AI and healthcare: The present and future of medicine