AI in Healthcare: 5 Practical Examples and Business Impact

This article is prepared by Alex Gurbych, Ph.D. in AI, CEO at Blackthorn.ai, and Healthcare & Life Sciences expert. 

Alex has over 15 years of experience in AI, machine learning, and software engineering. He’s led AI teams in healthcare, drug discovery, and smart city projects, with deep expertise in computer vision, NLP, and data science. Alex specializes in AI-driven innovation and practical machine learning solutions.

But first, a bit of  numbers to kick off the topic.

According to PMC, AI-driven healthcare technologies could cut annual U.S. healthcare costs by $150 billion by 2026, mainly by shifting from reactive treatments to proactive health management, like continuous monitoring, earlier diagnosis, and personalized care.

Statista’s surveys show an optimism around AI in healthcare. In 2024, about 73% of U.S. healthcare organizations believe generative AI will boost clinical productivity. Additionally, at least 60% expect improvements in patient experience and administrative tasks.

Yet, integration isn’t without its challenges. Nearly 45% of clinicians globally note AI’s inability to fully replace human judgment as a key drawback. Meanwhile, 41% express concerns about insufficient regulation around AI usage in clinical environments.

In the U.S., about 54% of adults worry about AI’s diagnostic accuracy. Around half are concerned about data privacy and security, while 43% see technical limitations as significant barriers. Despite these reservations, the evidence remains clear – AI, when paired with human expertise, substantially improves healthcare outcomes.

But let’s go more practical and see what AI use cases have real impact on the niche, all based on the projects developed by Blackthorn.

5 AI Use Cases in Healthcare: From Data to Diagnosis

1: Predicting Readmission Risks with AI

Predicting hospital readmissions has long been a tough nut to crack. About 1 in 5 patients end up back in the hospital within 30 days after discharge. It’s not only frustrating for patients but costly too – each readmission racks up between $15,000 and $20,000 in medical bills. And often, insurance won’t cover all of it. Even worse, frequent readmissions tend to signal worsening patient outcomes, increasing risks of complications and even mortality. So, how can we handle this better? The answer lies in AI-driven prediction.

Our team at Blackthorn.ai designed an intelligent service aimed at prevention rather than reaction. Instead of dealing with the consequences, we catch risks early. The AI tool we built identifies the likelihood of a patient’s return to the hospital, providing doctors and nurses with critical information for clinical decisions.

Clinical data from patients flow into a central database. Our AI analyzes this data and calculates the probability of readmission. It’s all neatly displayed in user-friendly dashboards – think of a Power BI layer on top of a sophisticated predictive engine. Nurses can quickly see risk levels for each patient, color-coded into red, yellow, and green bars. Green means things look good, yellow means caution, and red signals that extra attention is needed urgently.

The entire system functions across 3 critical points in the patient journey:

At initial onboarding, while the patient is still admitted, we gather basic demographics like age, gender, location, race, and clinical details. But at this early stage, we don’t yet have crucial data like the first PAC setting or a detailed risk assessment.

About 4-7 days later, once the patient is discharged, we enter the first risk evaluation stage. Now we know the patient’s first PAC setting and working DRG (Diagnosis-Related Group), which sharpens our early predictions. Still, we’re missing a detailed risk assessment.

As soon as the final DRG is available – essentially the definitive diagnosis – we can fine-tune our prediction even more. The system incorporates the final DRG code and discharge details to boost accuracy. But, again, the full risk assessment isn’t available yet.

Finally, right before the patient leaves care completely, the last stage kicks in. A welcome-home call is completed, along with an Initial Clinical Assessment (ICA). Now we have the final DRG, detailed discharge data, and the full risk assessment at hand. This means we’re fully equipped to give the most precise prediction possible, advising if the patient is genuinely ready to leave or needs further monitoring.

The practical benefits of using AI in this scenario are substantial. Our dashboard isn’t just data points – it highlights the specific features driving the predictions. Doctors see exactly why a patient’s risk might be elevated, whether due to age, prior episodes, or particular conditions. For instance, our insights revealed that patients between 30 and 50 have unusually high readmission rates, and the more hospital episodes someone experiences, the higher their risk climbs.

We also identified unexpected factors influencing readmission rates – like nurse performance. One nurse in our study had a notably high rate of patient returns. That finding wasn’t just interesting; it gave hospital administration something concrete to improve upon.

Beyond readmission, AI helps spot potential patient complaints and anticipate complications long before they appear. Imagine diagnosing cancer or chronic conditions before symptoms arise, allowing healthcare professionals to act early rather than react when it’s possibly too late. 

In short, the power of AI in healthcare goes beyond predictions on a dashboard. It equips medical staff to see around corners, helping them intervene at precisely the right moments. By predicting readmission risks and other health complications early, AI lets hospitals care proactively, keeping patients healthier and reducing financial strain across the system. And that makes all the difference.

2. AI Assistants for Medical Documentation

Doctors went into medicine to help patients, not to spend half their day with administrative overload and filling out forms. But that’s exactly what’s happening – physicians now spend 44.9% of their working hours dealing with electronic health records (EHR) and other medical documents. Wait times for consultations stretch from days into weeks or even months because healthcare providers are bogged down in paperwork.

AI-powered assistants offer a straightforward way out. Rather than doctors combing through stacks of documents before a patient visit, AI steps in and quickly summarizes critical information. Imagine a physician preparing to see a new patient. Normally, they’d have to sift through dozens of previous visit notes, medical histories, and hospital records. That could take hours, but with an AI assistant, it takes mere minutes.

Here’s how it works in practice: an AI assistant scans uploaded medical documents related to each patient. Doctors don’t need to read every line; they simply ask questions about patient history, diagnoses, or hospitalization. The assistant quickly retrieves and summarizes relevant data. If a doctor wants to double-check something, AI points them directly to the right spot in the original document.

But AI doesn’t stop there. Clinicians can also fill out medical forms using voice commands. This means less typing and more eye contact with the patient. Additionally, AI pre-fills medical forms based on diagnoses and clinical findings. Doctors then quickly review and adjust if needed, saving valuable time and reducing the risk of errors.

AI assistance is about giving doctors back time for what matters most: patient care. Less paperwork, more face-to-face interaction, quicker access to critical patient data. That’s a win-win scenario in today’s overloaded healthcare system.

3. AI-powered Patient Triage (Physician Assistant)

Doctors spend too much time handling repetitive tasks instead of caring for patients. Renewing prescriptions, answering routine questions about symptoms, handling complaints, or explaining basic test results – all these things pile up quickly. Receptionists, therapists, or general practitioners often spend hours dealing with this paperwork and routine inquiries, leaving less time for actual patient care.

An AI-powered assistant designed for patient triage solves this issue. It interacts with patients directly through chat, answering common questions and analyzing symptoms to determine the right specialist. Whether a patient needs a prescription renewed, has questions about pregnancy, or seeks advice about their child’s health, the AI assistant is available 24/7.

Here you can see another real application that we developed and delivered. It covers repetitive functions of general practitioners, such as renewing prescriptions, consulting on symptoms and complaints, discussing analysis and research results, and answering questions related to pregnancy, children’s health, and general well-being.

Doctors still have the final say – the AI sends concise summaries, allowing physicians to make quick, informed decisions. But by automating these routine tasks, medical professionals get back valuable hours to spend face-to-face with patients.

Simply put, AI takes care of the mundane tasks, letting doctors focus on what they do best: patient care.

4. AI for Medical Imaging Analysis

Medical imaging analysis is essential for early and accurate diagnosis, but interpreting scans like mammograms or ultrasounds is time-consuming and error-prone for clinicians. But there’s already a solution developed by Blackthorn AI. It’s an advanced AI-powered medical imaging analysis system designed to support doctors in detecting and diagnosing breast lesions.

Take mammography, for example. Our AI-driven platform automatically processes uploaded mammogram files, instantly detecting lesions and categorizing them as benign or malignant. It assigns each finding a severity rating along with confidence scores, giving physicians a clear idea of the analysis accuracy. For instance, a malignant anomaly detection immediately triggers an automated recommendation for biopsy. On the dashboard, doctors see clear visual summaries of detected lesions and suggested actions, allowing them to make rapid, informed decisions. With a single click, physicians can confirm recommendations such as biopsies or re-examinations, cutting down hours of manual analysis to mere moments.

The intuitive visualization is another crucial element developed by Blackthorn AI. We offer doctors two critical views: precise lesion locations highlighted directly on the mammogram, and intuitive heatmaps. These heatmaps show a density probability distribution – red areas indicate high likelihood of abnormalities. This dual approach saves significant review time and reduces oversight risk.
Our ultrasound analysis follows a similar logic. The AI analyzes ultrasound images, identifies lesions, categorizes their severity, and immediately flags whether a biopsy or a simple re-examination is needed. Each lesion is clearly segmented, visually marking the exact suspicious area on the ultrasound scan. Doctors see clear recommendations, such as “biopsy” for high-risk lesions, or “reexamination” for less certain cases. Confidence levels accompany each suggestion, ensuring transparency in how conclusions were drawn.

5. Generative AI for Advanced Medical Imaging Analysis

Cancer misdiagnosis is a serious problem – research shows mistakes happen in up to 44% of cases. Often, these errors occur because radiologists become fatigued towards the end of their workday, causing accuracy to decline sharply. Time constraints and limited availability of skilled radiologists further complicate matters. In many European countries, two specialists are required to confirm a diagnosis, adding to delays. And even then, results can vary widely because cancer diagnosis remains heavily influenced by personal biases.

To tackle these issues, Blackthorn AI introduced a powerful solution: generative AI for advanced medical imaging analysis. Unlike traditional imaging tools, generative AI immediately provides comprehensive consultations about a patient’s condition. It evaluates disease severity, assesses potential impacts on daily life, and offers personalized recommendations for treatment.

Consider a patient receiving X-ray results. Rather than puzzling over unclear medical images, they instantly see a clear explanation from the generative AI assistant. If the lesion is benign, the AI clearly suggests reexamination in 6 to 12 months, removing the guesswork. It even provides personal guidance addressing immediate concerns patients typically have, helping reduce anxiety around uncertain diagnoses.

The true strength of this AI approach lies in assisting radiologists, giving them an evidence-based second opinion on difficult cases. Studies indicate that doctors using AI alongside their expertise improve diagnostic accuracy by roughly 20%. Essentially, physicians who collaborate with AI achieve accuracy levels exceeding 90% – a critical improvement when every percentage point matters.

Can AI Replace Doctors? The Future of Artificial Intelligence in Healthcare

Throughout the article and analysis of each system we developed, we laid the idea that in fact, artificial intelligence is not a substitute for doctors at all. It is just a tool that will allow you to work more efficiently and interestingly. As mentioned at the beginning of the article, clinicians worldwide highlight five key concerns about AI in healthcare. First, 45% say AI can’t replace human creativity, judgment, or empathy, making it unreliable for patient interaction. Second, 41% cite a lack of regulation, raising safety and liability concerns. Third, 29% worry about accountability – if AI makes a mistake, who is responsible? Fourth, 23% fear AI bias, as algorithms trained on flawed data may perpetuate discrimination. Fifth, 17% warn AI relies on outdated data, risking incorrect recommendations. These concerns slow AI adoption, but solutions exist: clearer regulations, transparency in AI decisions, frequent data updates, and human oversight. AI works best as a tool to support – not replace – clinicians, helping them make better, faster, and more informed decisions while maintaining the human touch in patient care.

Check out Statista’s survey showing of clinicians who listed the following disadvantages of using AI worldwide in 2024