AI in Healthcare: Examples and Real-World Uses

Artificial intelligence is transforming the healthcare industry. Many of you know of large language models (LLMs) like ChatGPT but there’s so much more to AI.

In this article, we’ll show you the difference between LLMs and AI using real-world examples, including healthcare companies, some of which we’ve worked with. These examples will give you ideas on integrating AI into your company and using AI efficiently, so let’s break it down!

First Things First. Is AI = Large Language Models?

Large language models are just one portion of AI.

When people think of AI, many immediately think of ChatGPT and similar apps. These are extremely popular models because they’re convenient and easy to use. They belong to a category known as LLM (Large Language Models). LLMs focus on processing and generating human language.

However, artificial intelligence encompasses much more. AI utilizes machine learning algorithms, which detect patterns in data. There are also computer vision systems that interpret visual data, and robotics that handle physical tasks.

It’s important to note that all types of AI are equipped with unique applications, which highlight its diversity. The easiest way to visualize the uses is to look at our AI solutions, where each AI agent  — powered primarily by an LLM, but empowered with other technologies — handles a particular set of tasks:

• Document AI - improves operational efficiency in healthcare settings, minimizes errors and inconsistencies in data handling, and simplifies patient record processing and management.
• Decision AI - evaluates patient data through extensive analysis, improves the accuracy of diagnosis and treatment planning, and ensures patient data authenticity and confidentiality.
• Database AI - provides quick and efficient support to staff and patients, effectively manages and uses healthcare knowledge databases, and makes informed decisions with real-time data insights.
• Conversational AI - offers immediate and precise responses to healthcare-related inquiries, improves patient and staff engagement with AI-driven communication, and quickly accesses critical health insights to improve care delivery.

These make a great example of AI capabilities that can handle much more than LLMs alone.

Combining these can help any healthcare company enhance patient care, and free up time for patient-focused care and strategic initiatives by automating complex healthcare processes.

However, if you’re still interested in learning the capabilities of LLMs-only in the healthcare industry, we have a great post on it.

Artificial Intelligence in Healthcare: 15 Examples

1. AI-Powered Analysis for Healthcare Documentation

We recently helped a healthcare company label and analyze medical notes, ensuring less time spent on fixing processing errors.

With just one AI solution (Document AI) specializing in analyzing and labeling medical notes, the company now maintains a higher standard of patient care and operational efficiency with exceptional accuracy and speed.

Analyzing and labeling medical notes is a time-consuming task that’s highly prone to errors. To automate it, we trained an LLM to handle company-specific linguistics and data processing tasks.

As a result, the model understands and processes complex language in healthcare documents and adapts to different types of medical notes and terminologies without extensive retraining.

The AI agent improved the company’s data accuracy, processing speed, and cost efficiency - all key factors to improving the ongoing efforts of the healthcare sector to improve patient outcomes.

2. Freenome - Early Cancer Detection

Freenome combines machine learning with genomics and proteomics to identify cancer biomarkers in blood samples. Their AI models analyze complex biological data to detect early signs of cancer with high accuracy.

The goal is to enable earlier and more effective interventions, where this approach has a big potential to improve survival rates by catching cancer at a more treatable stage.

Freenome also uses a multi-omics approach where they examine multiple types of biological data simultaneously. Such an approach provides a more accurate view of biological changes associated with cancer.

3. Cleveland Clinic - Improve Clinical Workflows

‍Cleveland Clinic uses AI in the intensive care unit to enable early interventions by predicting patient deterioration. Advanced AI predictive model analyzes real-time data from ICU patients, including vital signs, lab results, and other health metrics.

Such AI use helps identify subtle patterns that may indicate deteriorating condition, often before it’s apparent to clinicians. Earlier detection allows Cleveland Clinic to intervene sooner, prevent adverse outcomes, and improve patient survival rates.

Cleveland Clinic also uses natural language processing to extract data from unstructured medical records, including physician notes, discharge summaries, and patient histories. Such use of AI speeds up the clinical decision-making process and improves its accuracy.

4. Easier and Faster Access to Healthcare Data

Another one of our clients needed an easier way to access healthcare data. It typically required analysts to manually process requests from non-technical users and translate them to SQL.

To enable non-technical users to access it directly, we developed an AI agent that automatically translates natural language to SQL (text-to-code) and trained it on the client’s database.

This helped reduce the response time by 90%, enabling a 1-minute query response without requiring programming skills.

Accelerated access to data speeds up the company’s workflows and showcases the effectiveness of AI-driven solutions in healthcare technology.

5. MD Anderson Cancer Center - Enhance Cancer Treatment and Research

MD Anderson Cancer Center uses AI to enhance cancer treatment and research by analyzing pathology slides and genomic data to identify personalized treatment options for cancer patients.

This company integrates AI into its ontology practices, which helps improve diagnosis accuracy and the effectiveness of treatments. The final result and benefit of such AI implementation is better patient care, outcomes, and advancements in cancer research.

Traditional methods of examining samples under a microscope are time-consuming and are subject to human error. AI combined with deep learning helps quickly and accurately identify cancerous cells in tissue samples. This way, MD Anderson Cancer Center’s patients can receive timely and precise diagnoses.

6. Butterfly Network - Interpretation of Ultrasound Images

Butterfly Network is a company that integrates AI into its portable ultrasound devices. Their most known ultrasound device is Butterfly iQ+, which helps clinicians acquire and interpret ultrasound images.

AI helps guide clinicians on probe positioning and provides real-time image analysis. Such a tool can help those with limited ultrasound experience to capture quality images and make informed clinical decisions quickly.
Such technology also helps democratize access to diagnostic imaging, which is useful in underserved areas. Real-time image analysis, and identification of anatomical structure and potential abnormalities help clinicians make swift decisions, which enhances diagnostic accuracy and patient care.

7. Automating Pharmacy Financial Analysis

One of our clients struggled to manually process PBM contracts, with their analysts spending considerable time extracting essential pricing, discount, and rebate information.

The process was highly inefficient and time-consuming, leading to decreased productivity and increased operational costs.

With no existing non-AI systems that could automate complex data extraction from PBM contracts, we opted for using an LLM and empowering with an OCR engine. We also developed an API for automated PBM contract processing and performed in-context learning using the client’s example documents.

The client now benefits from reduced information extraction time (from 10 minutes to 2 minutes) and an improved accuracy approaching 100% with reduced data extraction errors.

8. Kaiser Permanente - Preventive Care Strategies

Kaiser Permanente uses machine learning models to predict patient risk factors, tailor preventive care strategies, and enhance healthcare delivery and patient management.

The company developed an AI model that can identify patients at high risk for chronic conditions such as heart disease and diabetes. Such a system allows them for earlier intervention, where this proactive approach can help improve patient health outcomes.

At the same time, this reduces long-term healthcare costs and provides personalized care strategies. Such use of AI makes Kaiser Permanente a great example of AI’s potential to drive significant advancements in healthcare management.

9. PathAI - Pathology Diagnostics - Pathology Diagnostics

PathAI uses artificial intelligence to revolutionize pathology diagnostics by increasing accuracy and speed in interpreting biopsies and pathology samples.

Their platform employs machine learning algorithms that identify patterns indicative of cancer or other diseases, reducing human error or variability. This technology helps accelerate the diagnostic process and offers quick insight that facilitates timely treatment decisions.

PathAI enhances diagnostic accuracy and efficiency for greater patient care by using AI within clinical practice, leading to more precise diagnoses. Such an approach benefits pathologists, can save costs, and improves results for pathology and oncology practices.

10. Intermountain Healthcare - Support Clinical Decisions-Making

Intermountain Healthcare relies on AI to improve clinical decision-making and patient care, using algorithms to analyze medical images to detect abnormalities.
Such information helps radiologists in diagnosing conditions like cancer and cardiovascular diseases. With advanced AI algorithms, this company can predict patient admission and optimize bed management, which increases the hospital’s efficiency.

As a result, patients receive timely care, more accurate diagnoses, and timely interventions when required.

Intermountain Healthcare’s technology-driven solutions help improve leadership in delivering efficient, effective, and patient-oriented care much better than traditional methods.

11. Mass General Brigham - Improve Patient Care

Mass General Brigham employs AI in their clinical decision-making and patient care initiatives, using large datasets from EHRs and medical imaging to identify disease patterns and predict patient outcomes.

Through such AI deployment, Mass General Brigham also develops new treatments or improvements for existing therapies, especially within areas like cancer and neurology research.

This enables the Mass General Brigham to provide more tailored and effective patient care services. Therefore, this company is a great example of innovation in healthcare solutions and patient-centered care by using AI.

12. Stanford Health Care - Predictive Analytics (Precision Medicine)

Stanford Health Care deploys AI to improve patient care with predictive analytics, specifically for early detection of deterioration and sepsis, improving safety in critical care.

AI-driven precision medicine tailors treatments and therapies in oncology and cardiology by analyzing genetic and clinical data. As a result, Stanford Health Care optimizes diagnostic accuracy and treatment effectiveness.

Besides precision medicine, Stanford Health Care also streamlines data analysis, supports evidence-based decision-making processes, and personalized care across specialties. The benefits include timely interventions and personalized treatments that improve patient outcomes and quality of care.

13. Baylor Scott & White Health - Preventive Measures

‍Baylor Scott & White Health uses AI to analyze electronic health records, such as demographics, medical history, and clinical variables. With the help of machine learning algorithms, AI identifies patterns and trends that indicate potential risks for adverse events.

With patient-oriented care and preventive measures, Baylor Scott & White Health advances healthcare practices and positions the organization at the forefront of innovation in the healthcare industry.

14. Rush University Medical Center - Personalized Treatment Plans

Rush University Medical Center implements AI to identify high-risk patients and enable early interventions, as well as predict patient deterioration.

Artificial intelligence also helps RUSH to assist radiologists in the detection of diseases much earlier and more accurately.

They’re also able to utilize the same AI integration to analyze patient data to provide personalized treatment plans, particularly in chronic disease management.

15. Sutter Health - Clinical Outcome Enhancement

Sutter Health’s goal with AI is to enhance clinical outcomes and automate a great portion of their workflows. With AI models, they identify patients at risk of readmissions and complications, which allows for earlier intervention.

Using AI for imaging, Sutter Health also predicts diseases much more accurately. Following their workflow from start to finish, AI helps Sutter Health improve the patient care delivery process.

As a result, Sutter Health boosts its operational efficiency and positions itself as a leader in innovative healthcare solutions.

What Is the Main Advantage of Using AI in Healthcare?

The main advantage of using AI in healthcare is the ability to enhance diagnostics accuracy, improve patient care, and automate workflows.

Employing machine learning and deep learning, AI can analyze vast medical data beyond human capability. Such help improves the detection of diseases, allows companies to personalize treatment plans, and improves patient care and outcomes.

AI is of huge help in automating administrative tasks, which helps reduce the burden on healthcare professionals.

Predictive analytics can also help reduce health risks by intervening proactively, which ultimately reduces healthcare costs.

Most of the examples we’ve featured in this article use AI to achieve a higher standard of care, optimize resource allocation, and offer tailored treatment plans.

The results include an overall improvement in patient care and operational efficiency.

What Are the Risks of AI in Healthcare?

The main risks of AI in healthcare include biases in the algorithms, which can lead to unequal treatment and misdiagnosis.

AI systems require vast amounts of personal data, which also increases the risk of data breaches.

Overly relying on AI can reduce the critical thinking skills of healthcare professionals, and the lack of transparency in AI can lead to challenges in understanding and trusting AI-generated recommendations.

Lastly, there are also regulatory and ethical issues that must be addressed before the AI implementation to ensure safe and fair use.

Will AI Replace Healthcare Workers?

AI is very unlikely to replace healthcare workers, but it can significantly transform certain roles.

AI is good at handling repetitive tasks and analyzing large datasets, which helps assist healthcare workers in diagnostics, administration, and complex decision-making.

Therefore, AI is there to lift the burden on healthcare professionals and help improve efficiency and accuracy. This leaves more time for healthcare professionals to focus on patient-oriented care and complex decisions.

Also, AI can hardly replace ethical judgment, personal interaction, and empathy of the workers, so it’s best to consider it a powerful tool that supports healthcare workers.

Together, AI and healthcare workers can deliver higher-quality care and address the growing demands in the industry.

Automate Your Healthcare Workflows With Us

Would you like to automate your healthcare workflows and experience some of the benefits mentioned in these examples? Please schedule a 30-minute call with our experts.

We can discuss your needs, find the best way to integrate AI into your workflow, and show you how our healthcare AI solutions work live.