How AI Is Transforming Healthcare

Global healthcare systems are strained by aging populations, growing numbers of chronically ill patients, rising treatment and drug costs, and personnel shortfalls. Meanwhile, burdensome documentation requirements are contributing to physician and nurse burnout.

Rapid advances in predictive and generative AI are already improving how medical professionals, clinical researchers, and administrators at hospitals and insurers work, and they’re poised to deliver even more transformative changes in the coming years. These AI systems excel at spotting hidden patterns in large data sets, zeroing in on hard-to-discern details in medical images, supporting diagnoses in complex cases, and recommending operational improvements that may be applied to pare costs. These advances could lead to process reforms, productivity gains, and improved patient outcomes. Read on to learn about the benefits, challenges, and applications of AI in healthcare.

What Is AI?

AI uses complex statistical prediction models and large amounts of computing to help solve complex problems, understand and respond to natural language queries, create videos and other forms of online content, classify images, and more. Neural networks, including large language models, are trained on large amounts of historical data to construct AI models that can make predictions to help users anticipate and solve a range of problems. These models can also go back through their statistical parameters to correct errors and transfer their knowledge to draw inferences about new problems and domains. Large investments in the data centers and chips needed to train AI models and power their inferencing (the reasoning process they use to respond to user queries) have fueled the AI boom.

What Is AI in Healthcare?

Physicians, clinical researchers, pharmaceutical companies, and medical staff are using artificial intelligence technology to aid diagnoses, patient exams, drug development, and hospitals’ efficiency. Electronic health records (EHRs) have come into widespread use in US hospitals and medical practices over the past 15 years, in large part because of billions of dollars in federal incentives. While they’ve made recordkeeping more accurate and reduced medical errors, their unwieldy note-taking requirements, hard-to-navigate screens, and often superfluous alerts and inbox messages have also created extra work for healthcare professionals. AI agent–enhanced EHRs can help save clinicians time and increase patient face time by requesting that they generate summaries of patients’ conditions, medications, and lab results before exams, quickly jump to key functions, and speak or type natural language commands.

In radiology, AI systems can help spot areas of scans with the highest probability of abnormal tissue growth or measure specific indicators, such as changes in kidney volume, that can help physicians predict function declines before they show up in blood tests.

Many AI healthcare applications, however, are aimed at easing hospitals’ and medical practices’ administrative burdens—for example, by automating billing and scheduling, helping write prior authorization letters to insurance companies, or reminding a patient that it’s time for a mammogram. The healthcare IT sector is building GenAI systems that support diagnoses by analyzing patients’ histories, exam findings, and lab test results alongside reviews of existing bodies of knowledge about diseases, and reaching conclusions that can assist physicians on complex cases.

Key Takeaways

• EHRs in development are incorporating generative AI that lets doctors see summaries of patients’ charts and lab results and filter information relevant to specific ailments.
• Diagnostic tools using AI can save radiologists time and boost their accuracy by showing scan areas with the highest probability of cancerous tissue growth, or by measuring indicators that can help predict organ function declines before they show up in blood tests.
• AI can help draw conclusions from data in disparate sources—including EHRs, medical device outputs, and genomic test results—that could be relevant to research and care.
• In the back office, AI is can help billing departments maximize revenue, automate scheduling, remind patients of screenings, and draft prior authorization requests.

Benefits of AI in Healthcare

AI is poised to deliver a range of benefits in medical research, drug development, clinical diagnoses and care, and healthcare administration.

• EHRs: Electronic health record systems enhanced with generative AI can save physicians time by helping them prepare for appointments with concise patient summaries, simplifying system navigation, and automating note-taking.
• Diagnostic imaging: Hospital radiology departments are using AI to analyze medical images in order to help identify organ and other problems and aid in predicting diseases faster and more precisely than they could before.
• Hospital scheduling and planning: AI-enhanced scheduling systems can help administrators allocate personnel and equipment to where they’re needed most. Robots with onboard AI can learn new routines for the efficient delivery of medicines, lab samples, food, and other supplies.
• Clinical trials: Pharma companies are sifting EHR data on health outcomes and demographics to find clinical trial participants. Research work at the Cambridge Centre for AI in Medicine aims to find subgroups of patients among those in failed trails who did benefit from a treatment. Drug development in labs is benefiting from AI models that can spot patterns in the ways molecular compounds interact with pathogens that may make them suitable as candidates for further study.
• Medical research: EHRs contain copious typed notes with valuable information about treatments and outcomes, but that unstructured data has been hard to extract for research. Natural language processing can pull data from those clinical notes to help reveal drugs’ side effects or identify early warning signs of diseases. Industry standards such as Minimal Common Oncology Data Elements (mCODE) for cancer can make EHR data from different software platforms available to researchers so they can compare treatment options.
• Drug safety: The field may soon benefit from AI systems that mine clinical data stored in EHRs to measure medicines’ efficacy and risk among different demographic groups. Large language models, assisted by a technique called retrieval-augmented generation (RAG), can combine pharmaceutical company data sets to help find patients with a higher risk of adverse drug reactions.

Challenges of AI in Healthcare

Applying AI to EHR data doesn’t automatically result in improved insights, patient care, and hospital processes. Clinicians, administrators, and other staff need to trust the technology enough to use it regularly and be aware of the potential for mistakes. Financially strapped hospitals need to understand the high cost of cleaning up and anonymizing patient data so it’s ready to train AI models. Read on for more on these and other challenges.

• Doctor trust: Medical practitioners can be reluctant to use systems that could result in errors or replace their work. AI-based systems need to serve as assistants that help them improve care, moving carefully from low-risk use cases to higher-risk ones.
• Privacy rules: Strict rules for how healthcare data can be shared and accessed have limited the data available for AI model training. But global regulations are changing to let more healthcare data be used to train models and support medical decisions. For example, the European Health Data Space regulation establishes common formats for medical data and rules on reuse. The UK’s Data (Use and Access) Bill would make data on preexisting conditions, appointments, and tests accessible across the National Health Service regardless of which IT system created it.
• Data quality: Complete, and standardized medical data is essential to effective AI-based diagnoses and treatments. But the process of cleansing data to help ensure its quality can strain the finances of healthcare providers, especially hospitals and other practices with slim profit margins.
• Data silos: The lack of interoperability among EHRs from different vendors has limited data sharing among different healthcare providers. So has the lack of interoperability across systems at clinical research groups, pharma companies, and government organizations. Industry standards and government data exchanges are helping.

10 Use Cases and Examples of AI in Healthcare

Medical professionals are applying AI across a range of applications to improve diagnostic insights and clinical decisions, predict patient outcomes, and accomplish so much more. Here are 10 of the most common AI use cases in healthcare and life sciences.

1. Medical imaging: AI analysis of X-rays, MRIs, and CT scans can assist physicians in their diagnoses—for example, by identifying changes in kidney volume to predict function declines early. AI tools can help spot areas of scans with the highest probability of abnormal tissue growth.
2. Predictive decision-making: One operator of rehabilitation hospitals in the US is using AI models to help predict patient falls and flag discharged patients who may be at higher risk for readmission. Healthcare providers are also using AI to help make better decisions based on forecasts of disease risks and patient outcomes.
3. Clinical decision support: In a limited study published in 2024 in JAMA Network Open, medical diagnoses based solely on feeding six cases to OpenAI’s GPT-4 GenAI chatbot were far more accurate than those rendered by doctors using the chatbot only for assistance and those not using it at all.
4. Natural language processing: AI algorithms can automate note-taking during patient exams through voice recognition. They can also help extract insights from clinical notes.
5. Drug discovery: By screening molecular compounds in a pharma company’s library for efficacy, AI can help accelerate the identification of new treatments and therapies. It can also be used to help predict drugs’ safety and side effects.
6. Personalized medicine: AI algorithms can provide insight into individual patients’ responses to drugs based on their genetics, helping practitioners determine the optimal timing and dosage.
7. Administrative automation: AI-based software can simplify patient scheduling, follow-ups, billing, and documentation, as well as help providers cut costs by predicting needs for medical staff and equipment.
8. Remote patient monitoring: AI-enabled sensors and devices worn by patients can help doctors monitor patients’ cardiac, diabetes, cancer, and other conditions so they can intervene when patients don’t adhere to treatment, exercise, dietary, and other plans.
9. Virtual health assistants: AI-based chatbots can help advise patients on conditions and treatments and recommend lifestyle or dietary changes. Providers can also use chatbots to help patients schedule and prepare for appointments.
10. Robotic surgery: Surgeon-controlled, computer-guided systems of cameras, mechanical arms, and instruments, augmented by AI, can operate in some cases with greater precision than doctors using hand-held instruments, potentially leading to fewer complications, less bleeding, and faster recovery times. AI algorithms can also help plan robotic surgical steps, position instruments, and classify medical images.

What Is the Future of AI in Healthcare?

Further development and adoption of national and industry standards will help healthcare organizations and governments share more data, providing a stronger basis for AI-driven insights. But financially pressured hospitals will need to find ways to invest in the latest tools and prepare their data for AI analysis.

Likely to come into wider use are hospital robots that nurses and other staff control from their phones to help ferry lab samples, medical equipment, and supplies to shorten delivery times and free up staff time. EHRs that use GenAI to quickly get pertinent information to physicians at the right time and cut down on complex screen navigation are also starting to come to market.

Within the next decade, doctors will likely benefit from AI systems that help support medical decision-making during patient visits, suggesting diagnoses via a PC or tablet based on what the doctor has said, the existing literature, and data about similar past cases. The systems could also help recommend tests and medicines.

Modernize Healthcare with Oracle

Oracle Health products enhance various aspects of care, including through generative AI. They can help personalize workflows for staff, streamline managing patients, and provide relevant information before exams.

Oracle Health Clinical AI Agent captures doctor-patient conversations to generate draft EHR notes, and it lets doctors call up data from patients’ medical histories via voice commands. Oracle Health Data Intelligence lets providers and payers perform AI analyses on clinical and financial data. The services can prioritize high-risk patients, flag overdue screenings, and prompt patients to schedule appointments.

AI in Healthcare FAQs

How is AI used in healthcare?

Artificial intelligence is transforming numerous aspects of patient care and healthcare administration, including diagnostic support, personalized treatment plans, documentation, clinical trials, and hospital planning.

What is an example of AI in healthcare?

AI-enhanced healthcare software can quickly call up information about patients’ histories from electronic health records, help doctors more quickly document patient visits, assist pharma companies in designing clinical trials, and help hospitals plan staffing.