How Is AI Being Used In Healthcare Today?
From personalized treatment planning to predicting no-shows
How are companies using healthcare AI today? A better question might be: “How are they not?”
You’ll observe AI in clinical operations, streamlining patient scheduling and even helping providers tailor more personalized treatment plans. In short, AI in healthcare is being used or explored for utilization in nearly every facet of the industry.
Yet, what exactly does that look like? To help you understand the sheer scope, we’ll dive deeper into the specific areas of healthcare we’re seeing AI implemented. In addition, we’ll share what leaders can expect from healthcare AI in the future.
Explore how AI is being used in healthcare
Nowadays, AI is transitioning from buzzword to backbone; it’s actively re-shaping how we deliver, manage, and optimize medical services.
Healthcare leaders need to realize it’s no longer the differentiator—it’s the baseline for care moving forward.. With more than $30 billion having been invested in healthcare AI companies in just the last three years alone, it’s not going anywhere.
But let’s look at where it’s making the most impact right now.
1. Diagnostic support
One of the most visible applications of AI in healthcare is diagnostic assistance, helping radiologists save time and reduce decision fatigue.
Radiologists are tasked with manually reviewing every medical image and even with advanced systems like PACS, their workload keeps climbing. During the COVID-19 pandemic, some institutions saw imaging volumes surge by as much as 55%, amplifying burnout and extending turnaround times.
Now, machine learning algorithms can assist by analyzing medical images and flagging potential abnormalities for radiologists to review.
As one study explained, AI can:
Automate triage by sorting and prioritizing radiologic studies based on urgency
Improve the generation and interpretation of radiology reports
Amplify interdisciplinary collaboration and patient-radiologist communication by facilitating understanding.
Keep in mind that it’s not replacing clinical judgment, but helping radiologists catch things that might otherwise be missed.
2. Clinical decision-making and treatment planning
AI tools are also actively helping clinicians make more informed decisions by rapidly analyzing patient data from extensive medical research databases and case histories.
Nowadays, a practitioner treating a patient with complex symptoms can use AI to identify patterns, suggest differential diagnoses, or recommend evidence-based treatment protocols. This is particularly useful when combined with telehealth solutions in rural and underserved areas where specialists aren’t readily available.
A 2024 study highlighted that AI helps “improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making.”
3. Facilitates proactive care with predictive analytics
Another impressive observation in how AI is being used in healthcare is its role in shifting the industry from reactive to proactive care. Predictive analytics helps identify patients at increased risk of certain conditions before they become critical.
For example, this article mentions some of the ways that predictive analytics has been used:
Analyzing electronic health records (EHRs) to flag patients at risk for 30-day hospital readmissions
Detecting early sepsis in hospitalized patients using deep learning models
Supporting the self-management of diabetes in daily practice, which was shown to enhance patients' quality of life
Predicting complications from chronic conditions, like heart disease, before intervention is required
The ability to warn patients and intervene much earlier can lead to better patient outcomes and reduced health system costs. This also aligns directly with value-based care models, which prioritize patient outcomes and preventive interventions over volume-based services.
4. AI in clinical operations streamlines admin tasks
Patient care gets a lot of attention, but its role in optimizing administrative efficiency can’t be overlooked either. In the United States, administrative costs account for 15-25% of healthcare expenditures, meaning billions are spent on paperwork rather than direct patient care.
Fortunately, AI can help ease that burden. Consider the following ways it does so:
Documentation: Some AI tools can listen to patient-provider conversations and generate clinical notes in real time, freeing providers from hours of after-visit documentation.
Clinical notes review: One study found that AI could identify potential medication errors from clinical notes before they happened, leading to a 78% reduction in adverse events.
Scheduling optimization: AI-powered scheduling tools can predict no-shows, automate follow-up appointment booking, and reduce wait times by analyzing patterns in patient behavior and clinic flow. For healthcare companies, this could mean fewer empty slots and better resource utilization.
Prior authorization: AI can automate parts of this tedious process by cross-referencing treatment plans with payer requirements, flagging potential issues, and even drafting authorization requests.
Billing: Some AI tools can review claims for errors before submission, decreasing denials and speeding up reimbursement. Clinics may also experience greater reimbursements.
Staffing and resource allocation: Medical demand can fluctuate due to local outbreaks and seasonal illnesses. AI helps practices adapt to changes by forecasting surges, enabling them to provide adequate coverage without overspending on unnecessary shifts.
5. Personalized treatment and medication management
Remember, we brought up whole-health care earlier? AI is making it significantly easier for providers to look at the big picture rather than just focusing on symptoms. By analyzing genetic data, lifestyle factors, and treatment responses, AI can help identify which medications or therapies are most likely to work for a specific patient.
For example, research has found AI’s role in personalized treatment especially promising in areas like oncology, where it’s helped match patients to clinical trials based on tumor characteristics. Additionally, there are AI-powered apps that can monitor medication adherence and alert providers if a patient seems to be struggling with their prescribed regimen.
So, what’s the future of AI for healthcare companies?
How AI is being used in healthcare now will only evolve as the technology improves and continues gaining momentum. Its implementation in healthcare has been surprising so far, making what’s to come later almost unimaginable, but here’s what leaders can expect:
Greater integration: Expect AI to become embedded in everyday clinical workflows rather than existing standalone tools.
Enhanced interoperability: Silos will be broken down as systems learn to communicate more seamlessly.
Regulatory maturity: Right now, regulatory guidelines and standards are unclear, but they’ll (hopefully) become clearer as adoption increases.
A powerful combination: AI and the #1 telehealth infrastructure
The organizations most likely to succeed will include leaders who embrace AI and learn how to implement it strategically. However, the healthcare companies that’ll remain competitive for the long term will also incorporate virtual care.
At OpenLoop, we provide a white-labeled telehealth infrastructure that allows companies to implement AI-powered solutions at scale. In addition, we have a nationwide network of providers available 24/7 to accommodate comprehensive care delivery.
Interested in learning more? Contact us today!
*This content is intended for general informational purposes only and should not be construed as legal advice. For guidance on your specific situation, please consult a licensed attorney.