Artificial intelligence (AI) is revolutionizing the healthcare industry at an increasing rate. 73% of organizational decision-makers acknowledge that adopting AI will be essential in the next five years, and health leaders are already using AI tools to identify trends, create intervention strategies, optimize electronic health record (EHR) management, and more.
This article explores the potential of AI tools in healthcare, leading platforms for payers and providers to consider, and what to expect as the AI market expands.
Generative AI uses advanced algorithms to create new content to support enhanced decision-making and workflow automation. There are several generative AI models, but the two most commonly used in healthcare are:
Large language models (LLMs): A type of AI model designed to understand and generate human language, such as text generation or language translation
Generative adversarial networks (GANs): A class of machine learning model framework designed to generate new data, such as images or audio
Generative AI in healthcare has proven especially useful for administrative use cases, helping providers and payers automate routine communications, draft documentation, and surface relevant clinical context from complex records. It also plays a growing role in clinical summarization, which reduces cognitive burden by streamlining chart reviews.
The healthcare industry has long used predictive analytics to forecast health events and implement proactive care interventions. This type of AI tool evaluates current and historical data to make projections about future events.
Predictive modeling is the process of using mathematical algorithms to predict what will likely happen in the future. This process involves four stages:
Data collection: Gathering information from various sources, including proprietary structured and unstructured data, public health records, and cutting-edge technologies like wearable devices
Preprocessing: Cleaning data to ensure accuracy and relevancy
Modeling: Using data algorithms to make predictions and recommendations for specific patient populations
Interpretation of results: Moving forward with strategic decision-making and policy formulation based on data
Computers understand human language through natural language processing (NLP) technology, which is highly beneficial in helping health leaders extract insights from unstructured medical data. This AI uses algorithms to analyze and interpret text from medical records or notes.
AI tools offer wide-ranging capabilities that improve efficiency and drive better outcomes across the healthcare ecosystem. Providers can use AI to enhance clinical workflows and decision-making, while predictive models help payers stratify member risk and optimize utilization management.
Let’s look at a few specific applications and benefits of AI across the healthcare sector.
Health teams across the care continuum can use AI-powered analytics platforms to analyze their performance and identify opportunities for improvement. Specifically, AI tools can:
Streamline administrative tasks. Since roughly one-third of a clinician’s time is spent on activities outside of patient care, AI tools have a significant opportunity to streamline workflows by automating essential administrative tasks, such as patient scheduling and data management
Accelerate data analysis. LLMs, in particular, can automate the process of summarizing patient data by reading through EHRs to understand patients’ medical histories and identify trends or diagnoses
Reduce medical errors. Integrating AI into EHRs could reduce medical errors, such as misdiagnoses or prescription mistakes, by ensuring health teams don’t miss important information
Smaller organizations have a significant opportunity to enhance their resources and use AI to fill administrative gaps. Watch how Jake Hochberg, VP of Analytics and Chief Analytics Officer at Arcadia, explains it in Episode 4 of Unlocking Big Data:
AI tools have the potential to optimize workflows and efficiency across many healthcare functions.
Generative AI’s ability to analyze and glean insights from vast amounts of healthcare data can uncover hidden patterns and correlations not immediately apparent to human researchers. These insights can offer new understanding and breakthroughs, driving medical research through:
Drug discovery: AI can analyze the chemical structure of successful drugs and generate formulas for potential drug candidates, accelerating the drug discovery process
Clinical trial simulation: By creating virtual patient populations and identifying potential problems that may arise during clinical trials, generative AI can aid in predicting the safety of new drugs
Plus, this technology can significantly speed up research by automating data analysis and generating hypotheses for testing.
Health organizations can leverage generative AI in healthcare to reach more patients in the ways they feel most comfortable. Such tools enable providers to communicate with and serve more patients without adding more burden to current staff.
AI can help us with chatbots starting to reach out to patients more directly. Email is archaic; they don’t use it anymore. Calling people; they don’t answer the phone. But chatting they feel really good about.
This is especially helpful for sharing personalized educational content or interactive learning experiences. For example, generative AI can produce information about managing blood sugar levels to share with a patient with diabetes. The AI tool can also generate responses to the patient’s questions about their diagnosis, helping them better understand their condition.
Data analysis can inform decisions on patient care. AI optimizes this process by analyzing a patient’s genetic makeup and medical history to create personalized treatment plans that address each stage of a patient’s care:
Diagnosis: Researchers can train generative models to identify patterns related to diseases based on medical records and imagery
Treatment: Generative AI can apply its analysis to decision-making tasks, such as proposing a treatment plan. It can also help organizations make better decisions about which facilities or specialists to direct their patients to for care.
Follow-up: AI can leverage real-time data from monitoring devices to offer personalized recommendations based on patients’ adherence to post-treatment advice
In addition, analyzing patient data using AI can uncover social determinants of health (SDoH) and help patients overcome transportation or financial barriers that prevent individuals from getting care.
Despite AI’s clear benefits to the healthcare ecosystem, AI tools are not yet fully accepted and adopted across the healthcare sector. Hesitations result from:
Mistrust: Research suggests that the majority of patients are uncomfortable with providers using AI for care delivery, particularly due to disbelief that the technology can actually improve outcomes.
Limited staff capacity: According to market research by Arcadia and The Harris Poll, only 54% of IT leaders are confident in their ability to implement a solution successfully. Furthermore, 40% of health leaders cite a lack of talent as a top challenge for adopting AI, indicating a shift in the skills CIOs expect from their teams.
To address these obstacles, it’s critical to understand how AI tools work: They require accurate data to perform well.
By leveraging vast amounts of healthcare data, this technology can ultimately transform patient care for the better. Hochberg explains the importance of high-quality data for generative AI in the following clip from Episode 4 of Unlocking Big Data:
As explained in the video, a data analytics platform can deliver a high-quality data asset to AI models in two crucial ways.
First, aggregating and storing your data in an analytics platform ensures it’s cleaner.
Second, when data is in one centralized place, it’s easier to feed it into models and tools across your ecosystem.
Arcadia is a leading healthcare data platform offering an AI development tool that enables rapid data acquisition and aggregation at scale and in a single platform. This platform’s advanced analytics applications are built upon third-party benchmark data from CareJourney and customers’ own data aggregated by Arcadia, driving insights that support:
Predicting patients’ needs so that care teams can intervene without delay or uncertainty
Improving revenue through engaged and healthier patients, which leads to higher star ratings
Controlling costs by developing a network of providers that exceed cost and quality benchmarks
Driving efficiency and productivity by stratifying patients to help care managers focus in the right places
Arcadia was recently named #1 in Healthcare Data Governance and Analytics in the 2025 Black Book Client Ratings, and it’s easy to see why. This tool’s ability to deliver actionable insights and streamline data interoperability, thanks to a solid foundation of safe, accurate, and trustworthy data sets it apart from the competition.
Amazon Web Services' HealthLake enables health teams to understand, organize, and store raw health data in the Fast Healthcare Interoperability Resources (FHIR) industry-standard format. This analytics and AI solution uses machine learning (ML) models to extract structured data from unstructured text, which is ideal for large systems looking to modernize their data architecture.
N1 Health’s AI platform specializes in predictive modeling, combining clinical, social, and behavioral data to identify members’ health and engagement needs. With this technology, payers can build targeted patient personas to strategically plan engagement approaches that result in improved member satisfaction.
Atropos Health leverages the power of generative AI in healthcare to deliver real-world evidence and precision medicine insights. By accelerating access to evidence-based answers for clinical questions, Atropos Health empowers clinicians and analysts to make data-driven decisions about patients’ needs.
Persivia’s AI-powered tool combines real-time analytics with powerful AI models to automate workflows and improve outcomes. This integrated platform enhances collaboration between providers, payers, and members by integrating all functionalities needed for the care delivery process into one solution.
While the natural inclination in AI adoption is to select a tool that meets the user’s needs, the proper approach is actually quite the opposite. Even the most user-friendly AI tools can pose more challenges than advantages without longitudinal, context-rich data, which is why health leaders must instead prioritize a unified data platform.
Arcadia’s innovative data platform does more than store healthcare data — it transforms your organization’s data into actionable insights so you can deliver the highest quality of care. Through robust point-of-care integrations, Arcadia’s platform enables AI-driven insights that are both actionable and accessible directly from the systems clinicians already use.
As healthcare organizations confront financial challenges, workforce shortages, and health equity concerns, AI-powered analytics platforms offer powerful solutions to enhance resource efficiency and expand patient care capabilities. Accenture predicts AI can save the U.S. healthcare economy $150 billion annually by 2026, and other research projects AI can automate 15% of healthcare work hours.
AI tools in healthcare are no longer just a possibility — they’re a reality, and they’re transforming the industry. To keep up, healthcare organizations must implement technology that supports an unshakable data infrastructure and, therefore, informed decision-making.