AI in Healthcare: How Is AI Used in Healthcare and Future Trends

Artificial intelligence (AI), or intelligence demonstrated by machines, heavily influences many industries. Applications of AI in healthcare can change patient lives, improve diagnostics and treatment and help patients and healthcare providers make quick, informed medical decisions. AI in the global healthcare market was valued at $16.61 billion in 2024, the total value of products and services sold, and is projected to reach $630.92 billion in 2033.¹

During the COVID-19 pandemic, AI in healthcare was leveraged to identify and remove virus-related misinformation on social media. AI helped scientists expedite vaccine development, track the virus, and understand individual and population risk, among other applications.²

Companies such as Microsoft, which dedicated a $20-million commitment in 2024 to advance the National AI Research Resource pilot, recognize the need for and the extraordinary future of AI in healthcare.³

The ultimate goal of AI in healthcare is to improve patient outcomes by revolutionizing treatment techniques. By analyzing complex medical data and drawing conclusions before direct human input, AI technology can help researchers make discoveries.⁴

How does AI work in healthcare? Natural language processing (NLP) algorithms allow machines to understand and interpret human language. Machine learning (ML) algorithms teach computers to find patterns and make predictions based on massive amounts of complex data.

AI applications are already playing a huge role in healthcare, and their potential future applications are game-changing. Below, we’ve outlined four ways to combine AI and healthcare that are transforming the entire industry.

How is AI Used in Healthcare?

When combined, AI and healthcare have the ability to improve diagnostics, advance treatment options, boost patient adherence and engagement, and support administrative and operational efficiency.

1. Improving Diagnostics

Artificial intelligence in healthcare can help professionals diagnose patients by analyzing symptoms, suggesting personalized treatments and predicting risk. It can also detect abnormal results.⁴

Analyzing Symptoms, Suggesting Personalized Treatments and Predicting Risk

Many providers and organizations are incorporating AI in healthcare using intelligent symptom checkers. This machine learning technology asks patients a series of questions about their symptoms and, based on their answers, informs them of appropriate next steps for seeking care. Buoy Health offers a web-based, AI-powered health assistant that healthcare organizations use to triage patients with symptoms of COVID-19.⁵ It offers personalized information and recommendations based on the latest guidance from the Centers for Disease Control and Prevention (CDC).

Additionally, artificial intelligence in healthcare can take precision medicine—healthcare tailored to the individual—to the next level by synthesizing information and drawing conclusions, allowing for more informed and personalized treatment.⁶ Deep learning models can analyze large amounts of data, including information about a patient’s genetic content, other molecular and cellular analysis and lifestyle factors—and find relevant research to help doctors select treatments.

Healthcare AI is used to develop algorithms that make individual and population health risk predictions to improve patient outcomes. Researchers have discovered that a specialized AI algorithm, which tracks multiple patient variables such as vital signs and lab results, can identify sepsis before symptoms appear

Detecting Disease

Imaging tools can advance the diagnostic process for clinicians. Enlitic develops deep-learning medical tools to improve radiology diagnoses by analyzing medical data.⁸ This application of AI in healthcare allows clinicians to better understand and define the aggressiveness of cancers. In some cases, these tools can leverage virtual biopsies, which would aid clinicians in identifying the phenotypes and genetic properties of tumors.⁹

Smartphones and other portable devices may become powerful diagnostic tools that represent the future of AI in healthcare, especially in the areas of dermatology and ophthalmology. The use of medical AI in dermatology focuses on analyzing and classifying images and the ability to differentiate between benign and malignant skin lesions.¹⁰ Using smartphones to collect and share images could widen the capabilities of telehealth. In ophthalmology, the medical device company Remidio has detected diabetic retinopathy using a smartphone-based fundus camera, a low-power microscope with an attached camera.¹¹

2.  Advancing Treatment

AI in healthcare is becoming a valuable tool for treating patients. Brain-computer interfaces could help restore the ability to speak and move in patients who have lost these abilities.¹² This technology could also improve the quality of life for patients with ALS, strokes or spinal cord injuries.

In recent years, the use of AI in medicine has led to a growing number of studies showing that AI can enhance the prediction of immunotherapy effectiveness, advancing precision medicine.¹³ AI and healthcare technology advancements may be able to determine new options for targeting therapies to an individual’s unique genetic makeup. Companies like BioXcel Therapeutics are working to develop new therapies using AI tools and machine learning.¹⁴

Additionally, clinical decision support systems (CDSSs) can help healthcare practitioners make better medical decisions by analyzing past, current and new patient data. IBM offers clinical support tools to help a healthcare provider make a more informed and evidence-based clinical decision.¹⁵

Finally, one of the benefits of AI in healthcare is its potential to expedite drug development by reducing the time and cost of discovery. Artificial intelligence in healthcare can support data-driven decision-making, helping researchers understand what compounds should be further explored.¹⁶

3. Boosting Patient Engagement and Adherence

Wearables and personalized medical devices, such as smartwatches and activity trackers, can help patients and clinicians monitor their health. These AI and healthcare tools can contribute to research on population health factors by collecting and analyzing data about individuals.

These devices can also be beneficial in helping patients adhere to treatment recommendations. Patient adherence to treatment plans can be a factor in determining outcomes. When patients are noncompliant and do not adjust their behaviors or take prescribed drugs as recommended, the care plan can be ineffective. The ability of AI in healthcare to personalize treatment could help patients stay more involved and engaged in their care. AI tools can send patients alerts or content intended to provoke action. Companies like Livongo are working to give users personalized health nudges through notifications that promote decisions supporting mental and physical health.¹⁷

Artificial intelligence in healthcare can facilitate real-time patient monitoring and telepatient monitoring using wearable devices and sensors.¹⁸ A self-service model helps providers reduce costs and helps consumers access the care they need efficiently.

4. Supporting Administrative and Operational Workflow

AI in healthcare can improve administrative and operational workflow in the healthcare system by automating some of the processes. Recording notes and reviewing medical records in electronic health records takes a great deal of physicians’ time, making it one of the leading causes of lost productivity and burnout for physicians.¹⁹ Clinical documentation tools that use natural language processing can help reduce the time providers spend on documentation time for clinicians and give them more time to focus on delivering top-quality care.

Health insurance companies can also benefit from artificial intelligence in healthcare. The current process of evaluating claims is quite time-consuming. AI enhances fraud detection by identifying subtle patterns, enabling real-time analysis, and continuously improving accuracy through machine learning.²⁰ Natural language processing tools can help insurers detect issues in seconds rather than days or months.

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