5 Real-World AI Use Cases and Applications in Healthcare

Big pharma brands are juicing AI for all it’s worth. At Sanofi, AI drives faster, more effective clinical trials. Bayer uses AI not just in drug compound discovery, but also to enable improved self-care among the general population, and to optimize its own supply chain logistics. Meanwhile, a hospital chain in India, Apollo Hospitals, created a Cardiovascular Risk Score algorithm that predicts the risk of a person having a stroke or heart attack over a 10-year horizon. 

The fact that you’re reading this blog indicates that you’re curious about how you can leverage AI at your healthcare organization. You’re off to a good start. Research is the first step—if you want your AI investments to pay off, you need to understand where to apply an AI solution. 

You want the wins of healthcare brands that use AI efficiently, but you also want to side-step (or at least navigate with efficiency) popular obstacles and concerns. 

In this blog, we’ll explore some of the top AI applications in healthcare, examining their successes and potential pitfalls to provide insights for your organization in navigating this evolving landscape.

1. Virtual Assistants and Chatbots

This is typically the first step in a healthcare organization’s AI journey. If you’re planning to test AI at your hospital, pharma company, or diagnostics lab, with a chatbot, you are picking a fairly safe place to start integrating AI.  

Here’s why: Chatbots solve a fairly ubiquitous problem. Most organizations—especially in healthcare—receive a larger number of queries and requests on a daily basis than they can realistically handle with a human-only team.

Most often, your human team of receptionists is run off its feet. At some moments, they might be too busy to respond, or even if they do, they may not have the time or energy to display empathy and patience that one might expect in a healthcare setup. These human shortcomings are understandable, but they impact your patient experience negatively. 

AI chatbots can take the burden off your human team by handling basic queries and queuing only the most challenging questions and requests for your receptionists or call center team. By solving a real challenge, it's easy to ensure they deliver on your investment. 

Brands to emulate include Babylon Health which uses its AI chatbot to help patients decide whether to see a doctor or manage symptoms at home. Patients simply key in symptoms and receive an (instant) assessment. Babylon Health has already managed to slash unnecessary calls and appointments, freeing up medical care professionals or cases that truly need their attention. Another example is Woebot, an app that offers real-time support to people feeling anxious, depressed, or stressed, helping them combat larger mental health struggles in the long term.

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2. Drug Discovery and Research

This is the most common AI integration entry point for pharmaceutical companies, for obvious reasons. AI’s claim to fame is speed, and most pharmaceutical companies are in a race to release and patent drugs without spending billions of dollars. In some cases, the race to release viable drugs might even be more noble, like saving lives. For example, during the Covid-19 pandemic, many of us might remember the entire world keenly watching Pfizer and Astra-Zeneca as they attempted to identify and release a vaccine at the earliest. 

Pharmaceutical leaders to mimic in this regard, include Pfizer, that has been using AI for drug discovery since 2014. Given the decade-long lead they’ve had, Pfizer’s machine-learning models are already mature. Pfizer’s AI solutions analyze real-world patient data to detect patterns in adverse reactions. This helps them accelerate research for new treatments. In fact, AI also contributed to Pfizer’s rapid development of the COVID-19 vaccine, allowing scientists to model virus behavior and test vaccine candidates more efficiently.

3. Clinical Decision Support and Imaging

At research labs, decoding complex imaging data is a cost-heavy and error-prone process. AI can help improve profitability and accuracy by automating the imaging process. At UCLA, for instance, an AI model diagnoses prostate cancer with greater accuracy by mapping cancerous tissue. AI-assisted findings were found to be 45 times more accurate! 

There are a variety of deep learning AI applications transforming healthcare workflows out there. Like the prostate cancer example above, AI imaging tools assist can accurately identify breast cancer, brain tumors, and interstitial lung disease. Beyond cancer diagnoses, AI tools like Aidoc can analyze CT scans to detect critical conditions such as intracranial hemorrhages and pulmonary embolisms.

Outcomes from AI applications in such cases enable improved prioritization and reduce diagnostic delays—talk about saving lives!

4. Predictive Analytics

This is one of the most valuable applications of AI in healthcare and across a variety of sectors. From better managing pharmaceutical inventory and availability of beds in hospitals to seeing disease outbreaks before they become a problem, AI-enabled predictive analytics is a great enabler across healthcare verticals. 

For example, the NHS Vale of York Clinical Commissioning Group uses AI to identify patients at high risk of unplanned hospital visits. Their AI system flags at-risk individuals and connects them to health coaches for preventive care.

The result? A 36% drop in emergency visits and fewer avoidable hospitalizations. By stepping in early, AI helped ease pressure on the healthcare system while improving patient outcomes.

5. Robotic Surgery

Especially if you’ve implemented one or more of the above use cases, you might feel confident, convinced, and ready to let AI play a bigger role in your healthcare organization. Alternatively, suppose you want to build a roadmap of phased AI integration at your hospital. In that case, this use-case can represent a more mature level of AI integration: AI-assisted surgery can be one high-impact area to implement AI. In October 2024, surgeons at NYU Langone Health in New York City performed the world's first fully robotic double lung transplant on 57-year-old Cheryl Mehrkar.

The team made smaller incisions by utilizing the da Vinci Xi robotic system. It resulted in less pain and quicker recovery compared to traditional methods.

Post-surgery, Mehrkar experienced immediate improvements in breathing and expressed gratitude for the innovative procedure.

Pro tip: While a use-case like robotic surgery might seem like the most exciting and impressive, it's always better to start small, showcase value (and build confidence while also gaining learnings) before you embark on big-ticket AI projects. 

Overcoming AI Application Barriers in Healthcare

While all of these use cases might seem exciting, on-ground, pharmaceutical companies, hospitals, and clinics face challenges around seamlessly integrating AI into existing workflows. On the other hand, there are wins like AI accelerated drug discovery, reduced emergency admissions, and enhanced diagnostic accuracy.

And we cannot ignore how AI-driven precision is improving treatments, lowering surgical risks, and streamlining healthcare operations.

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You can eliminate AI hiring risks by accessing pre-vetted, enterprise-ready AI talent that integrates seamlessly into your healthcare workflows. HireCoder AI provides verified experts who deliver measurable results with tailored AI solutions—on time and at scale. So, contact us today to commence your organization’s AI journey.

FAQs

• How is AI transforming clinical trials?
  AI applications are reshaping healthcare workflows. In clinical trials, AI tools drive efficiency by identifying the right patient candidates, predicting drug success rates, and analyzing vast datasets in ways traditional methods can’t. AI models detect patterns, optimize trial designs, and reduce costly failures—helping new treatments reach patients faster, with greater precision, and at lower costs.
  
  
• Can AI predict disease outbreaks before they escalate?
  Yes, and it’s already happening. AI processes real-time hospital reports, social media activity, and environmental data to detect early warning signs of outbreaks. It has been used to track flu patterns, anticipate COVID-19 surges, and predict regional disease outbreaks—giving public health officials a head start on containment.
  
  
• Are AI applications replacing doctors in healthcare?
  No. AI is a powerful tool, not a substitute for medical expertise. It assists in diagnostics, treatment recommendations, and workflow automation, but final decisions rest with medical professionals. AI enhances speed and accuracy—helping doctors, not replacing them.
  
  
• What are the biggest challenges of AI applications in healthcare?
  Regulatory hurdles, data privacy concerns, and algorithmic bias remain significant obstacles. AI models trained on non-representative datasets can lead to inaccurate predictions, reinforcing healthcare inequalities. Addressing these challenges requires rigorous oversight, unbiased training data, and ethical AI governance to ensure patient safety and trust.