AI in Healthcare Examples: Diagnostics, Drug Discovery, and More

Check out top AI in healthcare examples to know how healthcare organizations are transforming everything from patient care to drug discovery and operations.

June 27, 2024
12 mins read
Last Updated July 05, 2024
AI in healthcare examples

AI in Healthcare Examples: Diagnostics, Drug Discovery, and More

As a healthcare provider, your ultimate goal is to deliver the best possible outcomes for your patients. However, in these times of data overload and resource constraints, achieving that goal can be difficult. Artificial intelligence can significantly help overcome these healthcare challenges.

By analyzing vast amounts of medical data, identifying patterns, and generating insights at a scale and speed that humans simply can’t match, AI is empowering healthcare professionals to make more informed decisions, streamline workflows, and focus on what matters most: providing exceptional patient care.

In this blog post, you will find real-world artificial intelligence in healthcare examples. We’ll cover various AI applications in healthcare, from streamlining diagnostics and drug discovery to enabling personalized medicine at scale.

Build intelligent healthcare solutions with Simform’s AI/ML development services. With expertise in using AI for healthcare specific challenges, our team can help you deliver the best possible patient care cost-efficiently. Contact us for a free 30-minute consultation.

AI in healthcare examples: How healthcare companies are leveraging AI in 2024

The potential of AI in healthcare is vast, but what does it look like in practice? In this section, we’ll take a closer look at how industry leaders are already using machine learning, natural language processing, and computer vision to transform every aspect of the healthcare journey – and how you can do the same.

A. Examples of artificial intelligence in patient care management

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1. Diagnostics and imaging

AI is revolutionizing diagnostics and imaging through deep learning neural networks, particularly convolutional neural networks (CNNs). CNNs can analyze medical images and detect subtle abnormalities in MRIs, CT scans, and X-rays with remarkable precision. These neural networks have shown promising results in tasks like classifying chest CT images for COVID-19, even with small datasets.

Additionally, automatic segmentation is a notable AI technique in medical imaging. AI segmentation algorithms, like the U-Net architecture, can accurately segment medical images into different tissue types, organs, or lesions. As a result, radiologists can save valuable time with precise quantification and analysis of anatomical structures and abnormalities.

Healthcare startups are already developing AI solutions for more efficient diagnostics and imaging. For example, Ezra’s FDA-cleared technology improves image quality and reduces scan time for early cancer detection, while Butterfly Network’s AI-powered, handheld ultrasound devices accurately detect lung abnormalities by identifying and quantifying B-lines associated with various pulmonary conditions.

Pro Tip: To manage the high volume and velocity of medical imaging data, implement edge computing techniques for real-time processing directly on imaging devices. This approach reduces latency and ensures the timely delivery of AI-generated insights to clinicians, which is crucial in time-sensitive scenarios like emergency departments or surgical procedures.

2. Clinical decision support

Clinical Decision Support (CDS) tools provide evidence-based recommendations, alerts, and reminders to help clinicians improve patient care, reduce errors, and enhance overall healthcare quality. 

Generative AI-powered CDS tools are one of the most popular examples of AI in healthcare. Here’s how they work:

  • Supervised learning algorithms are trained on large datasets to predict patient outcomes, readmission risks, or the likelihood of developing certain conditions.
  • Deep learning models (e.g., convolutional neural networks, recurrent neural networks) are used to analyze complex, unstructured medical data like imaging, EHRs, and genomic data for the discovery of intricate patterns and representations of diseases, patient characteristics, and treatment responses.
  • NLP techniques extract relevant information from unstructured clinical notes, patient narratives, and medical literature. This enables CDS tools to provide context-aware recommendations by understanding the nuances of clinical language.
  • AI algorithms process streaming data from electronic health records, wearables, and medical devices in real-time. This enables CDS tools to provide timely alerts, reminders, and recommendations based on patients’ current condition and treatment progress.
Simform developed a generative AI-powered CDS tool for more efficient medication planning

Simform partnered with a UK healthcare startup to develop a clinical decision support tool for optimizing medication plan. It uses machine learning algorithms like random forests and gradient boosting to analyze patient data (demographics, vital signs, allergies, medication history, etc.) to provide personalized medication recommendations. 

The HIPAA-compliant, cloud-based platform is hosted on Amazon Web Services (AWS) and uses services like EC2, S3, and RDS for compute, storage, and database management.

The tool integrates with EHR systems using HL7 FHIR standards and connects to pharmacy databases via APIs, enabling real-time drug interaction checks and dosage adjustments. It also employs NLP to extract insights from clinical notes and patient feedback, allowing for continuous improvement of medication recommendations based on outcomes. 

3. Personalized nutrition

AI and genomics facilitate personalized nutrition by leveraging advanced algorithms to analyze complex biological data.

Machine learning models identify patterns within genetic, metabolic, and microbiome datasets to classify patients into nutritional profiles, while deep learning networks uncover nutrient-gene interactions and predict individual responses to foods.

Genomic data obtained through nutrigenomic testing plays a crucial role in this process. By analyzing genetic variations, AI algorithms can determine how an individual’s unique genetic makeup influences their nutrient metabolism, absorption, and sensitivities. This information is then integrated with other biological data points to create a comprehensive nutritional profile.

Nutrigenomix offers genetic tests for tailored nutrition based on genes affecting metabolism, body composition, and eating habits, while Integrative phenomics crafts nutrition guidance using lifestyle and gut microbiome insights.

ADNTRO is another health tech company that offers DNA kits requiring only a saliva sample to help users discover personalized nutrition, training, predispositions to diseases, and more.

4. Remote patient monitoring

AI is now enabling continuous monitoring of patients outside traditional clinical settings. Through wearable devices, healthcare mobile apps, and smart home technologies, AI analyzes patient data to provide personalized care, early detection of health issues, and timely interventions.

Use Case AI Application
Remote health monitoring Machine learning algorithms analyze data from wearable devices to monitor vital signs (e.g., heart rate, blood pressure) in real-time, alerting healthcare providers to potential issues.
Medication adherence NLP algorithms in AI-powered apps send reminders to patients to take medication and understand/respond to patient queries.
Elderly care monitoring Computer vision algorithms analyze data from smart home devices (e.g., motion sensors, smart cameras) to monitor daily activities and detect falls, sending alerts to caregivers if necessary.
Mental health monitoring AI-powered chatbots or apps monitor patient conversations using sentiment analysis algorithms for changes in mood or behavior, providing support and alerting healthcare providers to potential issues.
Remote rehabilitation AI-powered VR/AR platforms using motion tracking algorithms guide patients through physical therapy exercises, tracking movements and providing real-time feedback to ensure correct form and progress.

With our deep understanding of AI technologies and their application in healthcare, Simform is at the forefront of developing cutting-edge remote patient monitoring solutions. We recently helped a healthcare startup build a system that enables doctors to track their patients’ health data daily. 

The tool utilizes non-invasive medical devices, such as blood pressure monitors, glucometers, and scales, to gather patient data, which is then transmitted through a mobile app to a centralized medical database for analysis by healthcare professionals. It aims to improve patient outcomes, reduce healthcare costs, and enable early intervention by providing doctors with real-time access to their patients’ health data.

5. Precision medicine

In precision medicine, AI analyzes vast amounts of patient data, including genetic, lifestyle, and environmental factors, to tailor treatments to individual patients. These algorithms can identify patterns and biomarkers that help predict disease risk, select the most effective treatments, and minimize side effects. 

Precision Medicine Stage AI Application
Risk Assessment ML algorithms analyze genetic data, biomarkers, clinical findings, and environmental factors to predict disease risk.
Screening You can then use ML models to tailor screening recommendations based on individual risk profiles. Neural network modeling optimizes screening procedures, ensuring patients receive appropriate tests.
Diagnosis Rapid whole genome sequencing, coupled with NLP for phenotyping, helps identify disease-causing gene variants, enabling earlier intervention and personalized treatment planning.
Staging and prognosis With predictive modeling, you can assess disease progression, severity, outlook, and recurrence risk using individual biomarkers.
Therapy selection AI guides therapy selection by analyzing multi-omics data, medical history, etc. Integrating biopsy imagery and biomarker tests improves treatment response prediction, enabling personalized plans for better outcomes.
Treatment monitoring AI monitors treatment safety, side effects, and response using biomarker data. For instance, AI models predict chemotherapy toxicity risk based on single nucleotide polymorphisms, enabling proactive management of side effects and treatment plan adjustments.

6. Surgical automation

Surgical automation is one of the most innovative (albeit complex) examples of artificial intelligence in healthcare. Surgical automation uses AI to assist surgeons before and during procedures. 

Before surgery, AI analyzes medical images like CT scans and MRIs to create 3D models of patient anatomy, helping surgeons plan the optimal approach and identify potential complications. AI also assesses patient data to predict risks and optimize care.

During surgery, AI-powered robotic systems like the da Vinci Surgical System enable precise, minimally invasive procedures by filtering hand tremors, scaling movements, and providing haptic (sensory) feedback. In fact, completely autonomous surgeries have also become a reality now. In China, the Centre for Artificial Intelligence and Robotics successfully trialed a robot that treats brain tumors. 

B. Examples of AI in drug discovery and development


7. Identifying new drug targets

One of the most prominent examples of artificial intelligence in healthcare is leveraging AI to streamline the drug discovery process and increase the likelihood of finding effective treatments by identifying new drug targets more efficiently. 

Machine learning algorithms can analyze vast biological datasets to uncover patterns and correlations between molecular data and disease states that might elude human researchers. This helps prioritize the most promising drug targets for further investigation.

Additionally, deep learning can predict a protein’s 3D structure from its amino acid sequence, revealing drug targets’ structure and function without costly experimental methods. This enables researchers to design drugs that precisely bind to these targets and enhance their effectiveness.

Atomwise uses its AI platform AtomNet to rapidly screen virtual compounds against novel drug targets, expanding the search space for finding optimized hit compounds to test further. 

Meanwhile, Iktos AI provides solutions for productivity improvement in small molecule discovery with techniques such as virtual screening, protein structure prediction, and drug repurposing.

8. Designing proteins with desired medicinal properties

Generative AI-powered solutions form a large part of AI in healthcare today. Generative AI models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), are being used to design novel protein sequences with desired medicinal properties.

VAEs compress protein sequences into a representation, learning important features and patterns. Once trained, they can generate new sequences by sampling from this compressed space. 

On the other hand, GAN’s generator and discriminator networks compete to create realistic protein sequences, with the generator learning to produce novel sequences resembling real proteins. 

By training on datasets of proteins with known medicinal properties, these models learn specific features associated with those properties. Researchers can then use the trained models to generate new protein sequences likely to exhibit desired medicinal properties. These AI-generated sequences serve as promising starting points for further experimental validation and optimization.

Cradle, a Dutch-Swiss startup, is using AI to accelerate drug discovery and development by creating software to design proteins with the properties drugmakers desire.

9. Accelerating clinical trials

AI is being leveraged to accelerate clinical trials by addressing key bottlenecks in patient recruitment and data analysis. Notably, platforms like Antidote use AI algorithms to match patients with the most suitable clinical trials based on their medical history, location, and other relevant factors. This targeted approach reduces recruitment time and costs while ensuring a better fit between patients and trials. 

Moreover, AI enables more efficient data analysis, helping researchers identify patterns and insights that can optimize trial design and outcomes. By automating manual processes and providing data-driven insights, AI allows healthcare organizations to streamline their clinical trial operations and bring new treatments to market faster. 

10. Enhancing vaccine development processes

Enhancing the vaccine development process has emerged as one of the most sophisticated artificial intelligence in healthcare examples. From identifying promising vaccine targets to predicting virus mutations, artificial intelligence is transforming how researchers develop vaccines. 

Here is a summary of AI uses in healthcare when it comes to the vaccine development process:

  • Identifying vaccine targets: AI algorithms can analyze large datasets on virus protein sequences to identify the most promising targets (epitopes) for vaccines that are likely to provoke an immune response. This helps accelerate the discovery of viable vaccine candidates.
  • Countering virus mutations: AI tools are being developed to predict how viruses may mutate over time. This allows researchers to design vaccines that target the most stable parts of the virus’s genetic code, enabling vaccines to remain effective even as the virus evolves.
  • Organizing research data: AI corpus like CORD-19 make the huge volume of scientific literature machine-readable and easily searchable for insights. This helps researchers quickly find relevant information to inform vaccine development.

While AI cannot replace all steps like clinical trials, it can help identify promising vaccine candidates faster, optimize trials, and produce vaccines that are more resilient to virus mutations.

C. AI in healthcare for admin and operations management

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11. Automated appointment scheduling

AI in healthcare applications is making the process of appointment scheduling more efficient and user-friendly. Chatbots powered by NLP allow patients to easily book, reschedule, or cancel appointments through conversational interfaces. 

Behind the scenes, machine learning algorithms crunch historical data to predict no-shows and cancellations, helping providers optimize schedules and minimize lost revenue. Sophisticated optimization algorithms like genetic algorithms and simulated annealing intelligently match patients with time slots based on factors like urgency, preferences, and provider availability.

Forward-thinking companies are already bringing these technologies together into comprehensive solutions. For example, Q-Flow AM offers enterprise-grade appointment scheduling that covers the entire journey from self-service booking to visit completion. Patients can schedule via desktop or mobile, while the system handles everything from calendar management and resource allocation to automated reminders and confirmation requests. 

12. Patient triage and routing

AI enhances patient triage by enabling more accurate, efficient, and accessible care navigation. 

  • NLP powers conversational interfaces that allow patients to describe symptoms in their own words. 
  • Machine learning algorithms then analyze this input alongside vast medical databases to intelligently assess symptom urgency and provide personalized care recommendations, whether that’s self-care guidance, virtual triage, or scheduling an in-person visit. 
  • Advanced AI triage solutions also incorporate computer vision to analyze patient-submitted images for more accurate recommendations.

On the backend, these AI triage tools integrate with electronic health records and appointment systems using Robotic Process Automation (RPA). This eliminates the need for manual intervention and ensures seamless integration between disparate systems.

For a leading US hospital, Simform built an AI-powered patient triage solution that uses AI to guide patients through personalized care journeys. Some key features of the solution are:

  • Virtual triage symptom checker: An AI-powered digital triage tool built using Python and the scikit-learn library for machine learning. It allows patients to check symptoms, self-triage, receive care guidance, and schedule appointments online.
  • Patient engagement chatbot:  A conversational AI assistant powered by OpenAI’s GPT-3 that handles routine patient requests like prescription support and insurance checks. This reduces the administrative burden on staff.
  • Seamless integration: APIs connect with existing EHR, scheduling, CRM, and other healthcare IT systems for easy implementation.

13. Billing and payment processing

By leveraging machine learning algorithms, AI can automate and streamline various aspects of the billing process. For instance, AI-powered systems can accurately extract relevant information from medical records, such as patient details, diagnoses, and treatments, to generate accurate and timely invoices. 

Moreover, AI-driven chatbots and virtual assistants can handle patient inquiries related to billing and payments. This facilitates quick and efficient customer support.

How Simform helped a health tech company streamline billing and payment processes

A workflow automation platform team partnered with Simform to build an all-encompassing solution for patients, healthcare providers, and staff to manage requests and payments. Simform used a suite of AWS services to build a secure, scalable, and HIPAA-compliant solution. Services like AWS Textract, ECS Fargate, S3, DynamoDB, Lambda, WAF, and Amplify were leveraged to extract data from unstructured healthcare records, process it efficiently, and safeguard the system.

The impact of the solution was significant – a 35% reduction in data extraction costs, 25% improvement in text extraction accuracy, 40% reduction in data extraction time, and 95% reduction in data breach incidents.

The platform streamlined healthcare billing and payment processes for the organizations that adopted it, enabling seamless management of service requests and payments between patients, providers, and staff.

14. Clinical documentation

We cannot leave clinical documentation out when it comes to artificial intelligence in healthcare examples. One prominent application of AI in clinical documentation is generative AI speech recognition systems that convert spoken clinical notes into structured, searchable text. This technology reduces the time clinicians spend on manual data entry, allowing them to focus more on patient care. 

Additionally, AI can analyze clinical documents to extract key information, such as diagnoses, medications, and procedures, which can then be used to populate electronic health records (EHRs) and generate standardized reports.

Microsoft’s Nuance recently launched its Nuance Dragon Ambient eXperience (DAX) product, which is a fully automated clinical documentation app. It captures clinician-patient conversations and translates them automatically into notes using GPT-4.

Another innovation in this field is Nabla Copilot, which uses GPT-3 to transform patient conversations into notes or other outputs. By listening to clinician-patient conversations, Nabla Copilot generates prescriptions, follow-up appointment letters, and consultation summaries, relieving clinicians of these tasks.

15. Fraud detection

Examples of AI in healthcare are not limited to diagnostics, treatments, and documentation. AI also facilitates fraud detection in healthcare by analyzing vast amounts of claims data to identify suspicious patterns. 

Machine learning models can establish baselines for providers’ typical claims volume, patient numbers, and billing amounts. AI algorithms then scrutinize claim details, checking for appropriateness given the diagnosis, procedure codes, and patient history.

By comparing claims to coding standards and principles, AI assigns risk scores to flag potential fraud for investigators. This technology can increase fraud detection by 2-3 times while reducing false positives by 10-20 times

While AI won’t replace human expertise, it greatly enhances investigators’ efficiency and effectiveness in combating healthcare fraud.

16. Supply chain management

AI is being increasingly used to streamline supply chain management in healthcare organizations in several ways:

  • Demand forecasting: AI algorithms can analyze historical data, seasonal trends, and external factors to predict future demand for medical supplies and pharmaceuticals more accurately.
  • Inventory optimization: AI-powered systems can track inventory levels in real-time, automatically reorder supplies, and optimize storage to minimize waste and reduce costs.
  • Supplier risk assessment: AI can analyze supplier performance data, financial stability, and external risk factors to identify potential supply disruptions proactively.
  • Predictive maintenance: AI can analyze data from medical equipment sensors to predict when maintenance or replacement is needed. This helps avoid unplanned downtime, extend asset life, and ensure the availability of critical equipment.
  • Logistics optimization: AI can optimize transportation routes, modes, and schedules to minimize costs and improve delivery times for medical supplies.

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In essence, the applications of artificial intelligence in healthcare are vast and exciting. But realizing these benefits involves significant challenges that must be carefully navigated.

Challenges in building AI-powered healthcare solutions

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1. Lack of quality, bias-free medical data

Patient data is often scattered across various systems, such as electronic health records, lab results, and imaging systems. To make matters worse, healthcare organizations frequently use different data formats and standards, creating a complex web of information that is difficult to integrate and use collectively. These factors make it challenging to create comprehensive datasets that AI algorithms can learn from.

Moreover, medical records often have missing or incomplete information, like unrecorded symptoms, treatments, or outcomes. This lack of data can lead to biased or inaccurate AI models, as the algorithms may not have a complete picture of a patient’s health status.

2. Patient data privacy and security

In many countries, healthcare data is subject to strict privacy regulations, like the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union. 

These regulations mandate that patient data must be collected, stored, and used in a secure and confidential manner. Failure to comply with these regulations can result in severe penalties and damage public trust. 

AI developers must navigate a complex legal landscape while ensuring that their solutions are both effective and compliant.

3. Interoperability and infrastructure concerns

Many healthcare organizations rely on outdated systems and software not designed to communicate and exchange data with modern AI applications. These legacy systems often use proprietary data formats and protocols. So AI developers must invest significant time and resources in building custom interfaces and connectors to enable data exchange between these legacy systems and AI applications.

Moreover, AI-powered healthcare solutions require robust and scalable infrastructure to handle the storage, processing, and analysis of vast amounts of complex healthcare data. Legacy systems lack the necessary computing power, storage capacity, and network bandwidth to support the demands of AI workloads. 

As AI solutions are deployed at scale, they must be able to process increasing volumes of data and support a growing number of concurrent users without compromising performance or reliability.

4. The need for educating staff and patients

As AI technologies are introduced into clinical settings, healthcare professionals must be trained to understand how these systems work, interpret their outputs, and integrate them into existing workflows. This requires not only technical training on the use of AI tools but also education on the underlying principles, benefits, and limitations of AI in healthcare. 

For example, physicians need to understand how AI algorithms arrive at diagnostic or treatment recommendations, so they can critically evaluate and contextualize the results before making clinical decisions.

Similarly, patients need to be educated about the role of AI in their care, including how their data is being used, the potential benefits and risks, and how to interpret and act upon AI-generated insights. Without proper education, patients may have unrealistic expectations about the capabilities of AI, leading to misuse or mistrust of these tools.

Build compliant, secure AI healthcare solutions with Simform

Simform is a trusted digital product engineering company with a proven track record of delivering cutting-edge AI/ML solutions for the healthcare industry. 

With a dedicated team of AI experts and a deep understanding of healthcare’s unique challenges and requirements, we are well-equipped to build custom healthcare AI models from scratch and fine-tune pre-trained models using state-of-the-art techniques such as transfer learning and domain adaptation.

Simform’s expertise spans a wide range of healthcare use cases, including disease diagnosis, patient risk prediction, and treatment recommendation, and we have successfully delivered HIPAA-compliant, AI-powered solutions for hospitals, clinics, and pharmaceutical companies. 

Our agile development methodology and strong focus on data quality and security ensure that your AI-powered application is built to the highest standards of accuracy and patient privacy, while cross-platform compatibility ensures that your healthcare professionals can access AI insights and recommendations whenever and wherever they need them.

If you are looking to improve patient outcomes, reduce costs, and streamline clinical workflows with AI-powered healthcare solutions, contact our experts today for a free consultation.

Hiren is CTO at Simform with an extensive experience in helping enterprises and startups streamline their business performance through data-driven innovation.

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