Updated: July 30, 2026

Healthcare providers are turning to AI to find new ways to support care, reduce administrative burden, and make better use of limited resources.

However, the challenge is identifying where AI can create meaningful value while maintaining the safeguards healthcare demands.

In this article, we define AI in healthcare, explore use cases and benefits, and cover some of the core implementation challenges.

What is AI in healthcare?

AI in healthcare refers to the use of AI models within applications and systems that can support work across clinical, administrative, operational, and research workflows, including services used directly by patients. Depending on the use case, these models can be used to classify images, extract information from records, estimate clinical or operational outcomes, or generate draft content from healthcare data.

A healthcare AI application typically combines an AI model with software that manages how it is used, controlled access to relevant data, integrations with electronic healthcare systems, and mechanisms for performance monitoring.

Some newer healthcare applications also make use of agentic workflows, in which the system pursues a defined goal by determining the next step based on the results of its earlier actions.

AI applications in the healthcare sector

AI applications in healthcare span a wide range of use cases in care delivery, administration, and operations.Some common examples include:

Medical imaging and diagnostic support

Healthcare organizations can use AI-enabled imaging applications to process medical images such as X-rays, CT and MRI scans, ultrasound images, retinal images, and digital pathology slides.

These applications can identify and localize suspected abnormalities, delineate anatomical structures or lesions, quantify features such as size or volume, and flag cases that may require time-sensitive review.

Qualified professionals can then use these outputs, alongside the original images and other relevant clinical information, to inform diagnosis or further assessment.

Risk prediction, triage, and treatment planning

AI-based predictive systems can use information from patient records, such as vital signs, lab results, recorded diagnoses, medication histories, and previous admissions to estimate the likelihood of a particular clinical outcome.

These estimates may appear as risk scores or alerts, which can help clinicians prioritize patients who may need closer monitoring or more urgent assessment.

Predictive models can also support treatment planning by estimating the likelihood that a patient will respond to a particular intervention.

Clinical documentation and information access

AI applications are often used to generate draft patient notes from transcripts of clinical encounters. Clinicians can then review and approve these notes before they are added to the patient record.

Healthcare organizations can also give AI applications controlled access to patient records and other approved information sources. This can make it easier for teams to summarize records, extract specific information such as medication histories or test results, and retrieve relevant policies, protocols, or medical literature.

Patient communication, monitoring, and care coordination

Healthcare providers can use patient-facing AI assistants to answer routine questions about appointments, services, or organizational policies. AI applications can also be used to prepare patient-specific after-visit instructions for authorized staff to review before sending.

Care teams can use AI-enabled monitoring applications to identify patterns that may indicate a change in a patient’s condition and warrant review. These systems can be designed to analyze periodic or continuous readings from wearable devices and home medical equipment, along with symptoms or assessments submitted through a patient portal or monitoring app.

Agentic applications can also be used to coordinate certain follow-up tasks across connected systems. For example, an application could interpret follow-up requirements from a patient’s discharge plan, check whether the required appointment has been scheduled, and create a task for the responsible care coordinator if it has not.

Hospital operations and resource planning

Hospitals and other inpatient care facilities can use AI systems to forecast patient volumes, admissions, discharges, bed occupancy, staffing needs, and supply requirements.

These systems can identify where capacity issues may arise by combining past demand patterns with current data such as scheduled procedures, emergency department activity, patient acuity, and estimated discharge dates.

Administrative and revenue-cycle workflows

Providers can use AI to extract and classify information from intake forms, referral documents, clinical notes, and claims. They can then use that information to support eligibility verification, medical coding, billing, prior authorization preparation, and claims validation.

For example, an AI application could suggest diagnosis or procedure codes based on a finalized clinical note, assemble supporting documents for a prior authorization request, or flag claims with missing or inconsistent information for staff review.

What are the benefits of AI in healthcare?

AI can create value across the healthcare system, from individual clinical decisions to organizational performance.

Some of the primary benefits include:

More timely and informed clinical decisions

AI can help clinicians access relevant information more quickly and analyze a broader range of patient data. This can reduce delays in assessment and support decisions informed by a more complete view of the patient’s condition, history, and recent results.

Better use of clinician time and expertise

AI can reduce the time clinicians spend on repetitive administrative work, including routine documentation and finding information across records and systems. This can give them more time for direct patient care and work that requires clinical experience, communication, and judgment.

Earlier intervention and better care coordination

AI can help care teams monitor patients more proactively by identifying changes that could indicate increasing risk. This can support earlier review and intervention where needed. AI can also help teams track pending actions across services and settings, reducing the likelihood of missed follow-up and incomplete handoffs.

More efficient operations and resource use

AI can help healthcare providers reduce administrative bottlenecks by automating routine processing and flagging missing or inconsistent information earlier. It can also support better use of staff, capacity, and supplies by helping teams anticipate and respond to changing demand.

How to implement AI in healthcare: Key challenges and considerations

Turning the potential of AI into safe and reliable healthcare applications involves several interlinked challenges.

Here are some of the main factors to consider:

Intended use and real-world validation

Organizations should adopt AI to address a clearly defined clinical, administrative, or operational need, rather than treating adoption as an end in itself. This means specifying how the application will fit into existing workflows, who will use or act on its outputs, and the baseline against which success will be measured.

Before wider deployment, organizations should evaluate the application under realistic operational conditions, such as through a controlled pilot or limited initial rollout.

Data quality and interoperability

Healthcare data is often fragmented across systems and varies in quality, structure, and completeness. This matters because inconsistent and incomplete data can prevent AI applications from functioning as intended.

Organizations should therefore assess whether the data required by an AI application is accurate, current, and appropriate for its intended use. They should also determine whether the data can be exchanged reliably between electronic health records, connected devices, and other relevant clinical or administrative systems.

Workflow integration and workforce readiness

AI applications can fail to deliver value if they disrupt established workflows, create more manual work than they eliminate, or are not adopted in practice.

Involving intended users from the outset can help ensure that the application actually supports their needs and fits into day-to-day work. Organizations should also provide role-specific training on how to use the application and interpret its outputs.

Privacy, security, and regulatory requirements

Healthcare AI systems often process sensitive patient data and connect to clinical or administrative systems that store or manage such information. Privacy, security, and regulatory compliance are therefore central to implementation.

Organizations should determine what data each AI application requires and grant it access only to that information. They should also define where the application will process and store data and how long it will be retained. For highly sensitive workflows, on-premises deployments can provide greater control over data location.

Some applications that support diagnosis or treatment may also be regulated as medical devices, depending on their intended use and the applicable jurisdiction.

Governance, human oversight, and monitoring

Once deployed, an AI system can influence consequential clinical, administrative, and operational decisions. Its use can also extend beyond the original scope as teams use the application for workflows or decisions it was not authorized to support.

Effective AI governance is therefore essential for keeping each application within defined boundaries and risk controls. Organizations should assign clear accountability for each application, define where human review or approval is required, and limit any actions it can take in connected systems. They should also monitor whether it remains within those boundaries, how well it performs, and its clinical, administrative, or operational effects. This allows the people accountable for the application to modify, restrict, or suspend the application when necessary.

Final thoughts

AI has potential applications across almost every part of healthcare, from clinical decision-making and patient monitoring to administration, coordination, and resource planning. These applications can ease pressure on limited staff and systems while supporting better patient care.

However, realizing this potential requires intentional and selective adoption. Healthcare is too regulated, complex, and consequential for sweeping transformation to be a sensible starting point. The organizations best placed to implement AI successfully will therefore begin with clearly defined use cases that are well suited to the technology, evaluate the impact of each application in real-world settings, and scale from there.

Learn more about Cohere’s AI solutions for the healthcare sector.

AI in healthcare: Examples, benefits, and implementation challenges