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The AI revolution isn’t just about supercomputers beating humans at chess or deepfake videos of politicians going viral. Beneath the surface, professionals across industries are already integrating AI into their daily work, reshaping how they approach tasks and solve problems. This quiet transformation is evident in healthcare, where AI is making inroads from the research lab to the patient’s bedside. With the global AI in healthcare market projected to reach $490.96 billion by 2032, both investors and healthcare insiders are betting big on AI’s potential to change medicine.

DeepMind’s AlphaFold has cracked one of biology’s big challenges by accurately predicting protein structures, potentially accelerating drug discovery and our understanding of diseases. Tempus is changing the face of precision medicine by using AI to analyze vast amounts of clinical and molecular data, enabling physicians to make data-driven decisions for personalized patient care and targeted therapies. While these applications represent breakthroughs with tangible impacts, AI in healthcare is still in its early stages. Read on to explore the current state of AI in healthcare and where it’s making meaningful differences for researchers, healthcare providers, and patients.

What is AI in healthcare?

AI in healthcare is the application of artificial intelligence to solve complex medical challenges— from decoding intricate patient data to guiding critical clinical decisions. Healthcare has long embraced automation for routine tasks like scheduling and billing, but AI pushes beyond these basics. It now fuels breakthroughs in medical imaging, speeds up drug discovery, crafts personalized treatment plans, and supports early disease diagnosis.

This technological advancement promises sharper diagnoses, fewer medical errors, and a future of more personalized care for patients. However, AI in healthcare is still evolving, with ongoing challenges in areas like data privacy, algorithmic bias, and integration into existing medical practices.

History of AI in healthcare

The roots of AI in healthcare trace back to the 1960s and 1970s, with early attempts at creating diagnostic systems and medical knowledge bases. These initial forays laid the groundwork for more sophisticated applications in the following decades, as computing power increased and AI algorithms became more refined. Today, AI is showing promise in areas like medical imaging analysis and drug discovery.

Here’s a timeline of a few developments that paved the path for present-day AI in healthcare:

• 1965: DENDRAL, one of the first expert systems, is developed to help identify unknown organic molecules.

• 1970s: MYCIN, an early AI system for diagnosing blood infections, is created at Stanford University.

• 2000: IBM and Sloan-Kettering begin collaboration on using AI to fight cancer, laying the groundwork for Watson for Oncology.

• 2012: Deep learning breakthroughs in image recognition begin to be applied to medical imaging analysis.

• 2016: Google DeepMind’s AlphaFold makes significant strides in protein folding prediction, crucial for drug discovery.

• 2018: The FDA approves the first AI-based medical device for detecting diabetic retinopathy without physician input.

• 2020: AI models are rapidly deployed to assist in COVID-19 diagnosis, treatment, and vaccine development.

• 2022: Large language models begin to show potential in medical research synthesis and clinical decision support.

10 use cases for AI in healthcare

The healthcare field includes an array of specialties and practices, and the emerging AI applications are equally broad in scope. AI is making its mark across the medical world, from simplifying administrative tasks to accelerating drug discovery. Research labs, startups, and scaleups are working to develop AI solutions for our most pressing healthcare challenges, aiming to improve patient care, refine diagnostics, and speed up medical breakthroughs.

1. Expediting drug discovery

Drug discovery has traditionally been a slow, expensive process, often taking over a decade and billions of dollars to bring a new medicine to market. Keytruda, an immunotherapy drug for cancer, took over 11 years to develop—research began in 2003, and it was finally approved by the FDA in 2014 for treating melanoma and later for other cancers. The sheer complexity of biological systems and the vast chemical space to explore have been major hurdles. AI is changing this landscape by rapidly analyzing massive datasets, predicting molecular behaviors, and identifying promising drug candidates.

DeepMind’s AlphaFold accurately predicts protein structures—a crucial step in understanding diseases and designing treatments. Pharmaceutical giants like Eli Lilly and Novartis are already using AlphaFold in their pipelines, potentially slashing the time and cost of developing new drugs.

2. Synthesizing medical research and information

The pace of medical research has exploded in recent decades, making it challenging for healthcare professionals to stay current with all the latest findings in their field. This information overload can lead to missed opportunities for improved patient care. AI is helping by quickly processing and analyzing medical literature, clinical trials, and patient data.

Zebra Medical, founded in 2014, uses computer vision to aid in diagnosing bone, liver, lung, and cardiovascular diseases. Their platform grants researchers access to millions of anonymized, indexed clinical records, enabling breakthroughs in disease patterns, treatment efficacy, and personalized medicine. The company, acquired by Nanox in 2021, also offers analytics solutions with algorithms and decision support tools to healthcare institutions.

3. Training health care professionals

AI is now supplementing traditional medical education methods like textbooks, lectures, and hands-on training. It can offer personalized, interactive learning experiences and realistic simulations that prepare healthcare professionals for real-world scenarios.

For instance, GoodLabs Studio’s Monkey Jacket 2.0 is an advanced AI-powered platform for medical training, featuring live AI patient avatars that provide ultra-realistic simulations for both human and AI physicians. The system improves medical examination skills through thorough validation and feedback, while generating high-quality medical conversation data and associated SOAP notes crucial for training specialized medical AI systems

4. Supporting medical imaging

Medical imaging is a key component of diagnosing diseases, but interpreting these images accurately and quickly can be challenging. Radiologists face increasing workloads and the risk of human error due to fatigue or oversight. AI is stepping in to assist, not replace, these skilled professionals. It can quickly analyze images, flag potential issues, and even prioritize urgent cases.

According to a 2023 report, the global AI in medical imaging market was valued at USD 0.98 billion and is projected to reach USD 11.76 billion by 2033, with a CAGR of 28.19% from 2024 to 2033. For instance, AI tool Mia (which stands for mammography intelligent assessment), developed by Kheiron Medical Technologies and Imperial College London, detects up to 13% more breast cancers in screenings than human radiologists alone.

5. Automating medical administration

Medical administration is a time-consuming aspect of healthcare, with professionals often spending hours on paperwork instead of patient care. This inefficiency can lead to burnout and reduced quality of care. AI is automating many of these tasks, freeing up healthcare providers to focus more on their patients. Here are some examples of companies in the AI health admin space:

• Nuance Communications’ Dragon Medical One uses AI for speech recognition, allowing physicians to dictate notes directly into electronic health records.

• Notable’s AI-driven healthcare platform offers pre-built and customizable workflows that streamline administrative tasks, cut costs, boost patient engagement, and increase team productivity.

• Thoughtful AI offers an AI-driven operating system for healthcare revenue cycle management teams, featuring AI agents that automate tasks like eligibility verification, prior authorization, claims management, and payment posting, aiming to reduce costs, improve efficiency, and maximize reimbursements.

6. Aiding disease detection

Early disease detection has always been a critical yet challenging aspect of healthcare. Subtle signs of serious conditions can be easily missed, leading to delayed treatment and poorer outcomes. AI platforms are being built to sift through patient data and medical images to spot potential issues that humans might overlook.

For example, Viz.ai offers an AI-powered care coordination solution that analyzes medical imaging data in real-time to accelerate diagnosis and treatment across various therapeutic areas. Their platform, Viz.ai One, uses FDA-cleared algorithms to detect suspected diseases from CT scans, EKGs, and echocardiograms, providing instant insights to healthcare providers and reducing time to diagnosis and treatment.

7. Improving patient care

Providing personalized, high-quality patient care has always been a challenge in healthcare, especially with increasing patient loads and limited resources. Doctors and nurses often struggle to give each patient the time and attention they need. AI is helping to bridge this gap by offering intelligent, automated support systems.

Ada Health’s AI-powered platform is a good example of this technology in action. Their app uses a conversational AI interface to gather symptoms from users and provide personalized health assessments and guidance. During the COVID-19 pandemic, Ada’s COVID-19 assessment tool helped people evaluate their symptoms and decide whether to seek medical care, easing the burden on healthcare providers.

8. Facilitating disease management

From diabetes to hypertension, managing chronic diseases has long been a complex and resource-intensive aspect of healthcare. Patients often struggle with consistent monitoring and treatment adherence, while healthcare providers face challenges in providing continuous, personalized care. AI is supporting these efforts with tools that enable real-time monitoring and personalized interventions.

A 2020 paper in the Journal of Translational Medicine found that AI is changing chronic disease management by enabling the analysis of vast, multidimensional datasets to provide personalized healthcare solutions. The integration of AI with deep phenotyping, multi-omics technologies, and wearable devices allows for more accurate disease prediction, tailored treatment plans, and personalized prevention strategies for conditions like diabetes, cardiovascular diseases, and cancer.

9. Empowering personal health management

Personal health management has evolved with AI technology, offering individuals personalized, accessible support to develop and maintain healthy habits. This digital guidance provides users with the knowledge and motivation to make lasting lifestyle changes, complementing traditional professional care with constant, on-demand assistance.

Here are a few examples of AI-powered personal health management tools:

• Healthify, India’s largest health platform with over 40 million users, uses AI-enhanced health coaching and tracking to help users achieve fitness goals and reverse metabolic diseases.

• Wysa, an AI-driven mental health app, offers anonymous support through automated conversations, aiming to help users manage stress, anxiety, and depression.

• Cal AI, available on iOS and Android, is an AI-powered calorie tracking app that allows users to calculate calories and nutrients by simply taking a photo of their food or scanning a barcode, using depth sensing and multimodal AI models to provide accurate results.

10. Augmenting health care provider capabilities

Healthcare providers need to balance precision and efficiency in their work, often under time pressure and with limited resources. AI may be stepping in to support healthcare providers, acting as a powerful tool to extend their capabilities.

For instance, researchers developed a system combining an optical tracker and stereo camera to monitor surgical instruments and breast surface changes during lumpectomies. The system uses computer vision to automatically track reference points, showing promise for continuous measurement of surgical tools in relation to breast movements during surgery.

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