AI in Healthcare: Examples, Use Cases, and Benefits

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Key Takeaways

• Technologies like data analytics, deep learning, predictive analytics, and content analytics support early diagnosis and care services.
• AI-driven predictive analytics optimize processes, reduce errors, and identify high-risk patients for early intervention, lowering healthcare costs and enabling institutions to handle more patients efficiently.
• AI in healthcare extends beyond data processing, offering benefits like enhanced diagnostic accuracy, personalized patient care, and efficient administrative task automation.
• Valued at $20.9B in 2024, the global AI medical market is forecasted to reach $148.4B by 2029, growing at a CAGR of 48.1%.

The increasing use of digital technology and reliance on data in the medical field presents impressive benefits of AI in healthcare.

As the number of patients grows, it generates vast amounts of health data that need to be processed and turned into actionable insights. This demand, coupled with the need for personalized medicine and digital health records, unleashes the transformative role of AI in healthcare.

Integrating AI and machine learning enables early disease detection and improved patient care. At Acropolium, we have diverse expertise in AI/ML consulting and solutions development. Serving the healthcare sector for 15+ years, we witnessed how the power of AI automation transformed medical business operations.

Today, we will guide you through global AI applications in healthcare, showing how artificial intelligence is used in healthcare businesses to save lives and cut operational costs.

What is Artificial Intelligence in Healthcare?

Widely adopted in industries globally, AI in healthcare encompasses the application of ML algorithms and cognitive technologies in medical settings. As the most prominent healthcare tech trend, AI analyzes medical data to predict outcomes and assist in diagnosis and treatment planning.

By using AI in the healthcare industry for patient data analysis, medical professionals can deliver more precise diagnoses and treatment strategies. Furthermore, AI enhances healthcare by analyzing big data to develop proactive preventive care recommendations for patients.

The medical sector generates vast amounts of data requiring automatic analysis to optimize hospital workloads and reduce costs. For that reason, artificial intelligence and healthcare go hand in hand, with the global AI medical market projected to reach $148.4B by 2029. In 2024, it’s valued at $20.9B with an expected CAGR of 48.1 toward the next four years.

Benefits of AI in Healthcare Operations

As medical organizations employ AL and ML algorithms to deal with big chunks of data, the number of AI benefits in healthcare is not limited to data processing alone.

Enhanced Diagnostic Accuracy

AI algorithms can analyze large datasets of medical records, images, and diagnostic tests to assist medical clinics in more accurate and timely diagnosis. This can lead to preventive disease detection and improved treatment outcomes.

Personalized Patient Care

By analyzing patient data, including medical history, genetics, and lifestyle factors, AI enables personalized treatment plans. Such AI solutions in healthcare are usually cloud-based, where the data is being collected on a medical platform or web application. Personalization can lead to higher patient satisfaction, contributing to improved diagnoses and outcomes.

Efficient Administrative Tasks

As part of a hospital management system, AI streamlines administrative tasks by automating scheduling appointments, managing medical records, and processing billing. This efficiency frees up healthcare professionals’ time, reduces administrative burdens, and minimizes errors, allowing them to focus more on patient care.

Cost Reduction through Predictive Analytics

By optimizing processes, reducing medical errors, and facilitating preventive care, AI can help lower healthcare costs for both providers and patients. By digitizing healthcare routines and switching to remote care models with telehealth apps, institutions can handle more patients a day with fewer resources needed.

Predictive analytics can also identify high-risk patients and intervene early to prevent costly medical complications.

Use Cases and AI Applications in Healthcare

The ways how one can employ AI for analysis and automation are defined by the complexity of their business and their core objectives. Here, we explore some of AI in healthcare examples from real life and applications that help medical businesses globally get the most out of their operations with advanced algorithms.

Medical Diagnosis and Treatment

AI aids in diagnosing diseases and recommending treatment plans. For example, IBM’s Watson for Oncology analyzes patient data to suggest personalized cancer treatments, improving clinical decision-making.

WFO has cataloged and organized literature, protocols, and patient charts. It has acquired knowledge from Memorial Sloan Kettering Cancer Center (MSKCC) test cases and experts. This allows them to employ computational reasoning methods to address specific cases.

Patient Data Processing

One of the core artificial intelligence use cases in healthcare is that it processes large volumes of patient data efficiently. For instance, Google’s DeepMind Health applies AI algorithms to analyze patient records, assisting clinicians in identifying patients at risk of deterioration.

In a specific case, their tailored AI system lowered false positives by 25% in a sizable UK mammography dataset compared to standard clinical workflows. With constant learning and use, it achieved this without overlooking any true positives.

Medical Imaging Analysis

Medical imaging is one of the most progressive AI healthcare applications. AI analyzes medical images, aiding in disease diagnosis and treatment. For instance, it identifies brain tumors in MRI scans, assisting surgical planning.

It detects Alzheimer’s and dementia in brain scans by assessing structural changes. Also, AI analyzes retinal images, detecting early-stage diabetic retinopathy, a cause of blindness in diabetics. GE Healthcare’s AI-powered Edison platform automates the analysis of MRI and CT scans, aiding radiologists in detecting abnormalities more quickly.

Electronic Health Records (EHRs)

AI technology is increasingly indispensable in EHR management. It enables the analysis of vast medical data to spot patterns that aid disease prevention and treatment. AI healthcare technology scrutinizes EHR data, identifying high-risk patients and devising personalized prevention strategies.

AI predicts disease risks based on medical history, lifestyle, and genetics, like diabetes or heart disease. It also flags medication patterns to prevent adverse drug reactions, suggesting alternative treatments. Oracle Health employs AI in its EHR systems to enhance data accuracy, streamline workflows, and improve patient care coordination.

Remote Patient Assistance

AI enables remote monitoring and patient assistance. This method emphasizes convenience, accessibility, and timely interventions, enhancing patient outcomes and reducing healthcare costs.

Patients access real-time medical support, share health data, and receive remote treatment guidance. Businesses utilize AI in the healthcare industry to analyze this data, identifying trends and potential issues early. This proactive and remote approach reduces hospital visits and fosters efficient healthcare delivery.

Thus, Philips’ HealthSuite Digital Platform uses AI to monitor patients’ vital signs remotely, allowing early intervention and reducing hospital readmissions.

Virtual Assistants and Chatbots

AI-powered virtual assistants and healthcare chatbots are vital in remote patient care, allowing medical staff to focus on complex issues. These tools provide personalized health advice, offer pharmacy suggestions, and aid patients in seeking medication information promptly. By offering swift and accurate responses, they reduce the need for patients to wait for professional guidance.

Patients can also use chatbots to schedule appointments and clarify upcoming visits. AI-driven virtual assistants provide medical advice and support. Healthily’s chatbot offers symptom checking and medical advice, improving patients’ access to healthcare services.

Drug Discovery & Vaccine Research

The use of artificial intelligence in healthcare and medicine accelerates drug discovery and vaccine development. It identifies potential drug candidates or vaccine targets more efficiently than traditional methods. In fact, AI algorithms can predict the effectiveness and safety of compounds, leading to faster development and testing processes.

Atomwise applies AI to the virtual screening of molecular compounds for drug discovery, expediting the identification of potential treatments.

Challenges and Considerations in Implementing AI in the Healthcare Industry

The rise of AI in the healthcare industry is propelled by several facilitating factors like abundant healthcare data and a trend of patients becoming more proactive consumers. Nevertheless, the landscape of the healthcare AI market isn’t without its challenges and obstacles, presenting hurdles that need to be addressed.

Data Privacy and Security Concerns

Organizations must ensure that patient data used for AI applications in healthcare is secure and compliant with regulations like HIPAA. To safeguard sensitive patient information, implement robust cybersecurity encryption methods and access controls. Regular audits and training programs can also ensure staff compliance with privacy regulations.

Interoperability and Data Integration

Healthcare systems often use disparate data sources and formats, making it challenging to integrate data for AI applications seamlessly. Those who plan to utilize AI solutions in healthcare should invest in interoperable systems and standardized data formats to facilitate data integration.

Application programming interfaces (APIs) can help streamline data exchange between different healthcare systems and platforms.

Ethical and Regulatory Compliance

AI applications in healthcare raise ethical concerns related to patient consent, bias in algorithms, and accountability for decision-making. Make sure to develop clear ethical guidelines and governance frameworks for AI use in your organization.

If you’re cooperating with a tech vendor, they must regularly audit AI algorithms for bias and ensure transparency in decision-making processes.

Lack of Skilled Workforce

To fully experience the benefits of AI in healthcare, a skilled workforce with expertise in data science, machine learning, and healthcare domain knowledge is required.

At this point, you’ll need to find a third-party partner to provide training programs and resources to upskill existing staff.

Why Choose Acropolium?

Acropolium is a seasoned web developer with a long track record of successfully delivering secure projects to the healthcare industry. Crafting GDPR-compliant software, we follow ISO-certified processes to guarantee unparalleled data security and regulatory adherence.

From on-demand applications and custom software development to legacy system modernization, our dedicated teams will elevate your operations with smart algorithms.

Acropolium Case Studies

As our company has been delivering AI/ML solutions to global industries, from healthcare and hospitality to logistics and fintech, we have some success stories to share. Let’s start with one of our most valuable AI in healthcare examples!

AI-based Data Quality & Data Profiling Tool

A top fintech firm sought automated data profiling, commissioning a new tool for data quality assurance and categorization. The tool was supposed to automatically gather, categorize, and allocate data while ensuring its integrity for valuable insights.

Solution

• The development team established standardized processes and frameworks for real-time data quality monitoring, identifying and tracking issues via dashboards and configuring alerts for immediate notification of changes.
• The system design was optimized for future growth and fluctuating data inflow and structured for horizontal scalability to manage increasing volumes without performance degradation.
• The real-time data quality monitoring tool promptly identifies and flags data quality issues.
• We used a proactive ML-based approach to ensure swift corrective actions to prevent inaccurate data spread.
• The scalable data quality tool and profiling software architecture include customizable features for modifying incorrect data detection settings.

Results

• Data errors decreased by 40%, achieving a final data quality rate of 95%.
• Additionally, data processing time was reduced by 30%.
• The client now processes up to 30 terabytes of data daily, showing a 200% improvement in scalability.

Bioscience Cloud-Based Big Data Processing App

A research-focused proteomics company specializing in discovering new biomarkers contacted us to seek the integration of AI and ML into its biomaterial analysis processes. Apart from employing advanced algorithms, we had to optimize their software’s back-end.

Solution

• We opted to structure the entire app development using the Scrum methodology, establishing a unified approach for managing task lifecycles.
• Daily stand-ups facilitated seamless communication among team members and external back-end developers.
• Our dedicated team ensured regular demos, which provided clients with insights into the project’s progress, enabling them to suggest product priority changes more effectively.

Results

• The project launch time was decreased by 30%.
• The client enhanced analysis processing accuracy by 40%
• Overall, we reduced analysis processing time by 38%.

Final Thoughts

Cutting-edge processing has revolutionized the medical industry, offering unparalleled benefits of AI in healthcare with improved patient outcomes and streamlined operations.

From personalized treatment plans and enhanced diagnostic accuracy to efficient administrative tasks and predictive analytics, AI continues to reshape the healthcare landscape. And Acropolium is here to help you discover the endless growth opportunities for your business!

From complex custom software development to consultation, we offer a flexible, subscription-based cooperation model that caters to any budget without compromising quality.

Sources of Information

• Artificial Intelligence Healthcare Market (2024-2029) — Markets and Markets