What is AI for healthcare?

Artificial intelligence (AI) for healthcare combines deep learning, machine learning, natural language processing, and a lot of data-driven insights that are revolutionizing how patients and providers view care.

The healthcare sector was in dire need of this improvement, which is probably why Fortune Business Insights projects that by 2032, the global market for AI in healthcare will reach $490.96 billion.

An AI solution for healthcare doesn’t just process mountains of data — it also understands it fully. It’s able to spot patterns we’d miss, predict outcomes we wouldn’t even think to check, and speed up decisions that used to take weeks.

With this cutting-edge technology, healthcare professionals can finally manage resources intelligently.

An overview of AI for healthcare

Artificial Intelligence (AI) is reshaping the healthcare industry, offering innovative solutions across diagnostics, therapeutics, drug development, and patient management. Its capacity to process massive datasets, detect complex patterns, and forecast outcomes is redefining how care is delivered — enabling more precise diagnoses, tailored treatments, and better health results.

Here’s a closer look at how AI is being applied in healthcare and the advantages it brings:

1. Diagnostics and imaging:

• AI-driven technologies are enhancing the interpretation of medical scans — such as MRIs, CTs, and X-rays — by identifying abnormalities with speed and precision.

• These intelligent systems assist radiologists in flagging suspicious findings earlier than conventional methods, leading to faster and more accurate clinical decisions.

• Beyond imaging, AI also predicts patient risks, medication reactions, and treatment outcomes, supporting more proactive, informed care planning.

2. Drug discovery and development:

• AI streamlines the path from research to treatment by uncovering drug targets, simulating compound interactions, and forecasting therapeutic potential.

• It can also support precision pharmacology by tailoring medications to individual patient profiles, boosting both safety and effectiveness.

3. Personalized medicine:

• By synthesizing data such as genomics, lifestyle behaviors, and clinical history, AI enables customized treatment strategies for each patient.

• This data-driven approach promotes higher efficacy, fewer side effects, and more relevant interventions based on personal health needs.

4. Patient engagement, care and monitoring:

• AI-based tools enable continuous health monitoring and deliver alerts in real time, supporting earlier interventions for chronic or acute issues.

• These ai-powered healthcare solutions also foster more personalized care experiences and can improve adherence by engaging patients directly in their health management.

• In administrative contexts, AI automates workflows — from appointment coordination to claims processing — allowing clinicians to devote more time to care delivery.

5. Disease surveillance and outbreak response:

• By aggregating and analyzing data from sources like EHRs, social platforms, and news feeds, AI can flag emerging health threats.

• This early detection capability strengthens public health responses, helping contain outbreaks and guide policy with data-informed decisions.

6. Cost reduction in healthcare:

• AI contributes to cost efficiency by supporting early intervention, refining treatment accuracy, and eliminating unnecessary services.

• It also reduces operational overhead by automating repetitive tasks and optimizing resource use across the care continuum.

7. Ethical and regulatory challenges:

• Despite its promise, the integration of AI into healthcare raises key ethical concerns — including patient privacy, bias in algorithms, and workforce disruption.

• Establishing robust oversight, transparent development practices, and inclusive data standards is critical to ensure that AI serves patients fairly and responsibly.

In summary, AI is emerging as a transformative force in healthcare — elevating diagnostic accuracy, accelerating drug innovation, enabling precision medicine, and enriching patient support. Still, thoughtful governance and ethical deployment are essential to realize its benefits equitably across all populations.

What is AI used for in healthcare? Common use cases

You now know what it is, but how is AI used in the healthcare industry? There are several different use cases for AI in healthcare, including: 

AI in patient experience

In addition to helping and assisting doctors and practitioners, AI can improve patient experience.

While we're seeing improvement in patient satisfaction scores, especially in areas like ambulatory surgery, the reality is that disparities persist. According to Press Ganey’s 2024 report, while scores are up overall, underrepresented populations still face significant gaps in care, particularly around empathy and personalization.

With AI, you can tailor care to fit each patient’s unique needs. Algorithms can spot patterns and predict health risks by looking into patient data like their medical history, lifestyle factors, and genetics. This is often called precision medicine, which means that much more personalized and tailored treatment plans can be made.

On top of that, telehealth services powered by AI offer the opportunity to shift how doctors and other professionals interact with patients radically.

By automating routine tasks like appointment scheduling, sending lab results, and even providing 24/7 triage through virtual assistants, AI frees up precious time for healthcare professionals and ensures that patients have endless access to the help they need.

AI for healthcare around the globe

AI is bridging gaps in access to quality care worldwide:

• Telehealth powered by AI enables rural communities to connect with specialists.

• Natural language processing tools translate care instructions into local languages.

• Mobile AI screening kits detect diseases in low-resource regions without full hospital infrastructure.

These applications show that AI patient engagement tools can improve health equity and support sustainable global health systems.

AI for healthcare across clinical specialties

Beyond general patient care, AI for healthcare is delivering measurable results in specific medical fields:

• Cardiology: AI-powered ECG analysis can detect atrial fibrillation or early heart failure signs days before symptoms appear.

• Oncology: AI reads mammograms and CT scans for cancer with accuracy comparable to — and sometimes exceeding — human radiologists, enabling earlier intervention.

• Dermatology: Image-based AI can flag skin lesions likely to be melanoma, prompting timely referrals.

• Radiology: AI triage tools prioritize cases of stroke, brain bleeds, and fractures for rapid physician review.

• Ophthalmology: AI-assisted retinal scans detect diabetic retinopathy in routine eye exams, helping prevent vision loss.

These use cases prove AI is not only enhancing workflows but directly improving patient outcomes across the care spectrum.

AI for prevention and population health

AI in patient care isn’t limited to treatment — it’s also reshaping prevention strategies. 

Predictive analytics can:

• Forecast seasonal flu outbreaks using population health data.

• Analyze wearable device data to detect heart attack or stroke risk in real time.

• Provide personalized recommendations for diet, exercise, and medication adherence for chronic diseases like diabetes, COPD, and hypertension.

This shift toward proactive health management helps reduce hospital admissions, improves quality of life, and keeps healthcare costs in check.

AI in healthcare data management

The healthcare sector is facing an overwhelming data explosion. Unfortunately, much of this valuable information remains siloed, disconnected, and underutilized, which slows down progress in patient care and hinders advancements in research, drug development, and even accurate diagnoses.

AI's ability to sift through, analyze, and synthesize massive amounts of data in real time is changing this. We no longer have to wait years for meaningful insights to pop up.

AI insights for healthcare are gained quickly thanks to powerful technology breaking down these barriers, connecting previously isolated data points, and creating a flow of information.

Types of AI used in healthcare

Let’s now get into the different types of AI powering healthcare. Each one brings a unique set of strengths to the table:

Machine learning (ML)

ML is about teaching algorithms to learn from data. For those who work in healthcare, this means feeding systems with data like patient records or imaging results to find patterns or predict outcomes.

For example, ML can flag early warning signs for chronic diseases or optimize treatment plans.

Deep learning

Now, deep learning takes it further. While ML works on patterns, deep learning uses neural networks modeled on the human brain. These networks tackle complex problems like interpreting medical images or predicting disease progression based on genetics.

Natural language processing (NLP)

Next up is natural language processing, or in other words, AI technology that understands us. It’s designed to make sense of human language, whether spoken or written.

Without this type of AI, healthcare professionals would have to read through mountains of patient records, clinical notes, and research papers to gain actionable insights.

Robotic process automation (RPA)

Finally, robotic process automation takes over repetitive tasks like billing, scheduling, or processing insurance claims. This isn’t glamorous, but it’s essential for an industry that’s so overworked. RPA frees up time, allowing clinicians and administrators to focus on their patients.

Benefits of AI for healthcare providers and patients

There doesn’t seem to be a person alive that hasn’t heard about AI. It’s been stealing headlines for years, trending in almost every industry imaginable.

This is because it is revolutionary for people and industries and has the potential to transform how we live and work.

Here are some of the advantages of using it in healthcare:

1. Reduced staff burnout

Burnout among healthcare staff is reaching a breaking point. With the combination of shrinking workforces and more than half of U.S. adults — 51.4% — reported managing two or more chronic conditions, healthcare providers are under a lot of pressure. 

However, with AI, a solution is offered by advancing resource allocation. Through real-time prioritization, AI ensures that emergent conditions are addressed quickly at every patient touchpoint.

AI-enabled communications APIs demonstrate this in action. They enable seamless communication across voice, video, and messaging platforms to support healthcare use cases.

Patients can book appointments, access results, or connect with providers remotely, all through intuitive digital tools. Conversely, doctors and specialists can collaborate in real time, sharing data and insights to provide faster, more accurate care.

2. Streamlining operations with AI in healthcare management

AI-powered healthcare management systems can automate not just appointment scheduling, but also:

• Medical coding and billing, reducing claim errors and denials.

• Insurance pre-authorization and benefits verification.

• Shift scheduling based on patient volume forecasts.

These tools free healthcare staff from administrative burdens, allowing them to focus on high-value patient engagement — a critical step in improving patient experience in hospitals.

3. Improve patient-provider communication

Poor communication is one of the biggest pain points in healthcare. A recent study analyzing HCAHPS data from 3,200+ U.S. hospitals found that patient-provider communication — especially about medications and discharge instructions — consistently ranked among the lowest-rated aspects of hospital care, underscoring how deeply such communication breakdowns undermine the patient experience.

Now, why is that still the case in the mid-2020s? We have the tools to change this, and AI is at the forefront of that change. Technologies like natural language processing (NLP), predictive analytics, and speech recognition have resulted in big changes and advancements in how we communicate with patients. 

These tools both improve the clarity of our conversations and let us tailor information to each patient's unique situation, making shared decision-making more meaningful and effective.

We can deliver these conversations more securely, trusted, and privately with Branded Calling. 

4. Better patient outcomes

AI’s ability to improve outcomes is undeniable. For instance, studies show that AI tools can reduce the length of stay for conditions like intracranial hemorrhage and pulmonary embolism by streamlining diagnosis and treatment plans. But that’s just the start.

The difference is staggering when you compare a hospital equipped with AI tools to one without. AI helps to give patients the best possible chance at recovery, no matter how strained the system might be. 

Challenges of AI for healthcare

With all good things, there are challenges. And, when we’re discussing the use of technology in an industry that can quite literally save lives, the stakes are even higher. So, be sure to take these considerations into account:

Access to quality data:

With healthcare data expected to grow, we face exciting opportunities and significant challenges. The issue, however, isn’t the sheer volume of data. No, it's its quality and accessibility. It must be more cohesive across many systems, creating inconsistencies and inaccuracies. So, you must look at your infrastructure that connects and integrates this information. TIP: A Vonage feature-rich platform with telehealth integration can help improve connection and collaboration between colleagues and clients.

Cost of infrastructure:

The Cleveland Clinic, for instance, has made remarkable strides with their AI-powered Command Center, enhancing patient flow and processing rates by providing real-time forecasting of bed capacity, staffing levels, and OR schedules, helping streamline transfers and optimize resource use.However, achieving this level of AI adoption demands a lot of financial investments in infrastructure, compliance, and training. Working with experienced partners who streamline the process can ease the financial burden.

Seamless integration with health IT systems:

One of the most significant barriers to AI in healthcare management is compatibility with existing electronic health record (EHR) platforms like Epic, Cerner, or Allscripts.

Standards like FHIR (Fast Healthcare Interoperability Resources) are critical for enabling AI to pull and analyze data from multiple systems in real time.

Vonage’s API-driven approach makes it easier to embed AI patient engagement tools into existing workflows without costly overhauls.

Acceptance of new technology:

The real barrier to adopting AI in healthcare (and in other industries) is the lack of human trust in this new tech. A 2025 peer-reviewed survey of nearly 300 U.S. healthcare professionals identified increased job demands — specifically needing to manage more complex tasks — as a key driver of resistance to adopting AI-supported technologies.The way forward here is education, transparency, and technology partners who can help prioritize projects and guide or manage implementation. We must show clinicians how AI can integrate smoothly with their existing workflows and benefit them.

Building trust through ethics and compliance:

For AI to reach its full potential, it must operate within strict healthcare compliance frameworks. Solutions should meet HIPAA, GDPR, and emerging regulations like the EU AI Act.

Healthcare leaders must also address concerns about AI bias, ensuring algorithms are trained on diverse, representative datasets. The American Medical Association’s “augmented intelligence” stance emphasizes keeping clinicians in control while leveraging AI’s speed and scalability.

By prioritizing transparency, explainability, and strong governance, healthcare organizations can boost clinician and patient trust in AI systems.

When did AI solutions for healthcare become popular?

To answer this, we will have to look into the history of AI within this industry. Let’s go back in time together, back to the 1970s. People were grooving to disco hits like "Stayin' Alive," the first Star Wars movie was just around the corner, and the world was still processing the moon landing.

But at the same time, AI for healthcare solutions was first used to assist with biomedical issues.

Since then, the number of AI-powered applications has expanded enormously and is continuing to adapt to help the industry reduce spend, improve patient outcomes, and increase business efficiencies overall

How AI for healthcare is reshaping decision-making

AI is also shaking up decision-making in healthcare, and it’s about time. Gone are the days of guesswork or treating everyone the same with a one-size-fits-all approach to medicine.

Thanks to predictive models, doctors can now identify high-risk situations before they even happen. AI also lets providers compare a patient’s data with a bigger picture (like a whole population, for example), catching patterns that would otherwise slip through the cracks.

It doesn’t just help with immediate care (although that’s a big benefit). AI is boosting research, too, and helping identify disease risks and trends. It’s all about smarter, more personal, and more efficient care.

The rise of AI agents in patient care

From AI to improve patient experience in hospitals to remote monitoring, autonomous AI agents are emerging as the next frontier. Examples include:

• Virtual triage assistants that assess symptoms and recommend next steps.

• AI surgical robots for precision procedures.

• ICU monitoring agents that detect subtle signs of patient deterioration before humans can.

By combining autonomy with human oversight, these tools promise faster interventions and safer outcomes.

The future of AI in healthcare

Today, we’re already seeing glimpses of a patient journey powered by AI, but tomorrow, this technology will become the backbone of personalized, preventive care.

It will be possible to monitor every step of your health in real time using various tools, from wearable devices analyzing blood sugar trends to AI predicting and notifying providers of early disease indicators.

But let’s go further. AI will transform how care is delivered, making it seamless and intuitive. Your voice could check you into clinics using healthcare communications solutions, and your data (secured, de-identified, and ever-learning) will feed a system that treats individuals and identifies global health trends.

This isn’t some distant dream but the trajectory we’re on, where human expertise meets computational genius.

AI for healthcare: Reduce burnout, enhance patient care, and improve operational efficiency

At Vonage, we believe that everyday encounters make all the difference in the healthcare industry. These things include patients being able to book appointments without waiting days, weeks, or sometimes even months to be seen by an actual person or having access to the information they need to make decisions.

Vonage Communications APIs can enhance patient care, streamline provider operations, and improve outcomes, all while reducing costs and making healthcare more accessible.

Ready to elevate your healthcare apps? Check out Vonage AI-enabled healthcare solutions today.