Artificial Intelligence (AI) in Healthcare: 9 Use Cases

Elena Besedina, Project Manager
Top 9 Use Cases for AI in Healthcare

AI in healthcare is transforming the industry, much like how food production automation has revolutionized the agricultural sector. The abundance of digital health data, the demand for personalized medicine, and the need for cost reduction drive this growth. An aging population with a higher prevalence of chronic diseases also impacts.

These and many other emerging demands trigger advanced approaches to digitalizing healthcare. In particular, AI and ML algorithms analyze historical health data, enabling early disease prediction and timely intervention. 90% of industry leaders believe AI is crucial for their organizations to stay competitive. 80% said they try new things early or quickly adopt them after testing.

As an AI-powered software provider, Integrio wants to share valuable insights on this topic. This article will explore the transformative power of AI in the healthcare industry. Stay tuned to discover its most significant trends, challenges, benefits, and top use cases.

Artificial Intelligence in Healthcare in 2023

Artificial Intelligence is changing the way healthcare works. It analyzes lots of medical information, like patient records and images, to find essential data and help doctors make better decisions.

Using AI in healthcare supports various stakeholders:

  • Patients

    AI empowers patients by providing personalized care, early disease detection, and remote monitoring capabilities.

  • Healthcare providers

    AI automates administrative tasks, enhances diagnostic accuracy, and optimizes treatment plans.

  • Researchers and scientists

    With the help of AI, researchers analyze large datasets, identify patterns, and accelerate drug discovery.

Now, what trends are setting the development of Artificial Intelligence in healthcare in 2023? Let's consider the most notable ones.

  • AI-powered Robotic Process Automation

    Robotic Process Automation (RPA) solutions streamline administrative tasks. They facilitate processes, reduce mistakes, and save money, leading to more accurate patient treatments. Intellibuddies use RPA solutions to speed up check-in and check-out, improve inventory, and enhance equipment management.

    For example, T-Systems, an organ-tracking service provider, monitors time and location to ensure smooth transplant operations.

  • Emotion AI

    This approach brings hope to mental health disorders and autism. AI systems analyze facial expressions, tone of voice, and other behaviors to diagnose and monitor these conditions.

    For example, a virtual avatar therapist Ellie detects depression and post-traumatic stress disorder (PTSD). It analyzes words, facial expressions, posture, and gestures. Also, Google Glass helps people with autism understand social cues and emotions.

  • Data-driven and personalized services

    AI enables the analysis of large datasets, including medical records and genetic information. As a result, it simplifies the creation of personalized treatment plans and interventions. Prognos harnesses AI and Big Data analytics to improve disease prediction and management.

    For instance, Tempus analyzes molecular and clinical information for cancer patients. It generates personalized treatment recommendations and predicts therapy responses.

  • Drug discovery

    AI transforms the way we discover new drugs. It analyzes biological and chemical data to find patterns and predict potential drug candidates’ performance. It speeds up treatments’ development and makes the process more cost-effective.

    For example, Deep Genomics analyzes genetic data to identify potential therapeutic targets, predict drug response, and develop personalized treatment strategies. Exscientia, in turn, autonomously designs and prioritizes potential compounds for synthesis and testing, accelerating the identification of promising drug candidates.

  • Ambient Intelligence in healthcare

    AI systems seamlessly fit into the healthcare setting. They collect and understand data from different sources like wearables, sensors, and electronic health records. It enables constant monitoring, early identification of health problems, and proactive actions to improve patient care.

    For example, Philips Healthcare offers a range of Ambient Intelligence solutions that collect and analyze data from various sources. They include remote monitoring, personalized care, and proactive interventions. BioBeats develops wearable devices and mobile apps that track physiological signals, such as heart rate variability and sleep patterns.

The Challenges of Adopting AI in Healthcare

Implementing AI in healthcare comes with several challenges. Addressing these issues requires a multidimensional approach. It involves collaboration between healthcare providers, regulators, policymakers, technology developers, and patients. Here are the most widespread problems one might face.

  • Data Availability and Quality

    AI relies on vast amounts of data for training and analysis. However, healthcare systems often face challenges accessing and integrating diverse datasets from various sources. As a result, ensuring data quality, completeness, and standardization is often a complex task.

  • Privacy and Security

    Healthcare data contains sensitive patient information, making privacy and security crucial. Implementing AI requires robust measures to protect its confidentiality and comply with specific regulations.

  • Ethical Considerations

    AI raises ethical concerns, such as the responsible use of patient data, potential algorithm biases, and the impact on patient-doctor relationships. Ensuring transparency, fairness, and accountability in AI systems is essential to build trust and address ethical challenges.

  • Integration with Existing Systems

    Integrating AI technologies into an existing healthcare infrastructure is sometimes tricky. Consider compatibility issues, interoperability between different systems, and adapting to legacy software.

  • Regulatory Issues

    Healthcare is a highly regulated industry. Establishing clear guidelines and frameworks for AI in healthcare, including validation, certification, and liability, is essential yet complex due to the rapidly evolving nature of AI.

  • User Acceptance and Training

    Successfully adopting AI requires acceptance and trust from healthcare professionals and patients. You should train healthcare providers in AI technologies, address concerns about job displacement, and guarantee effective collaboration between systems and human experts.

Top 9 Healthcare AI Use Cases

AI is revolutionizing healthcare in many ways. It brings advancements in diagnosis, treatment, research, and patient care. Around 35% of healthcare startups in the EU that utilize AI focus on distant monitoring. Meanwhile, disease diagnostics closely follow at 33%.

Let's discuss these and other AI advances in healthcare in greater detail.

  • Disease Diagnosis and Risk Prediction

    AI tools analyze patient data, including medical images like X-rays, CT scans, and MRIs. Also, they study vast genomic datasets to identify variations, mutations, and disease-associated genetic markers. ML algorithms can identify patterns and risk factors. They enable early detection and personalized treatment plans.

    Examples: Aidoc, Tempus, Zebra Medical Vision, and Deep Genomics.

  • Remote Patient Monitoring

    Use AI for continuous remote patient monitoring. Wearable devices and sensors collect vital signs, activity levels, and other health-related data. This way, healthcare providers detect changes, intervene when necessary, and provide tailored care.

    Examples: Current Health, Biofourmis, and Medtronic.

  • Drug Discovery and Development

    The technology transforms drug discovery by analyzing vast biological and chemical data. It helps identify potential drug candidates, predict their efficacy, and streamline development. As a result, AI accelerates the discovery of new treatments.

    Examples: Atomwise, Recursion Pharmaceuticals, Exscientia, and Insilico Medicine.

  • Personalized Medicine

    AI simplifies the creation of personalized treatment plans tailored to each individual's unique characteristics. The technology can determine the most effective interventions, dosage, and pathways for better outcomes by analyzing patient data and genetics.

    Examples: 2bPrecise, Genoox, Sophia Genetics, and Deep Genomics.

  • Virtual Assistants and Chatbots

    AI-powered virtual assistants and chatbots provide immediate support and information to patients. They answer common health-related questions, provide symptom assessments, and offer guidance on self-care measures.

    Examples: Buoy Health, Medva, Cool Blue VA, and Ginger Desk.

  • Robot-Assisted Surgery

    Thanks to this approach, doctors enhance precision, control, and efficiency. Surgeons use AI-powered robotic systems for complex procedures, leading to improved outcomes and reduced recovery time.

    Examples: Intuitive Surgical, Hugo™ RAS system (Medtronic), Mako (Stryker), and Senhance System (TransEnterix).

  • Early Warning Systems

    An AI tool analyzes patient data to predict and identify high-risk individuals, such as those at risk of readmission or developing complications. Early warning systems alert healthcare providers to potential health deterioration, enabling proactive interventions.

    Examples: Epic Systems, CLEW Medical, Bernoulli Health, and ClosedLoop.

  • Health Records Management

    AI streamlines and improves electronic health records (EHRs) management. It automates tasks like data entry, coding, and documentation, reducing administrative burdens and improving accuracy.

    Examples: Cerner, Athenahealth, Health Catalyst, and Allscripts.

  • Mental Health Support

    AI, ML, and natural language processing (NLP) tools also assist in mental health diagnosis and treatment. Emotion AI analyzes facial expressions, voice tones, and other behavioral cues to identify and monitor disorders. As a result, patients get improved personalized care and interventions.

    Examples: Ellipsis Health, Woebot, SilverCloud Health, and Ginger.

Benefits of AI in Healthcare

AI brings numerous benefits to the healthcare industry, transforming service delivery and improving patient outcomes.

According to Insider Intelligence, AI is effective in improving clinical outcomes (59%), operational performance (58%), health system efficiency (53%), financial outcomes (47%), administrative performance (46%), and consumer engagement (45%).

Let's overview the most crucial advantages in detail.

  • Improved Diagnostic Accuracy

AI algorithms in healthcare significantly improve diagnostic accuracy by analyzing large amounts of patient data and medical images. Therefore, medicians make more accurate diagnoses. It leads to earlier detection of diseases, fewer misdiagnoses, and better planning.

  • Enhanced Treatment Personalization

AI brings a personalized touch to healthcare by carefully examining patient data, genetics, and medical history. This information tailors treatment plans to suit each individual. Such an approach results in targeted interventions, optimized medication dosages, and improved outcomes.

  • Efficient Administrative Processes

AI handles scheduling appointments, managing billing, and handling data entry. It lightens the administrative workload for healthcare staff. Also, such solutions boost operational efficiency and give specialists more time and energy to focus on providing quality patient care.

  • Precision Medicine Advancements

AI is a helpful guide, matching patients with the most suitable clinical trials and treatments based on their unique characteristics and genetics. It advances precision medicine, providing personalized therapies that target people’s specific needs, leading to better outcomes.

  • Data-Driven Insights

AI algorithms process vast amounts of healthcare data. They extract valuable insights to manage population health, monitor diseases, and allocate resources effectively. AI aids in identifying trends, optimizing resource usage, and enhancing public health strategies.

  • Accelerate Development

AI speeds up finding new drugs by analyzing big datasets and detecting potential candidates. It helps create new treatments faster, saves money, and streamlines searching for effective therapies for different diseases. For example, algorithms suggest potential therapeutic AI applications for drugs already approved for other indications.

Improve Patient Care and Operational Efficiency with Integrio

AI integration in healthcare holds immense potential to revolutionize the industry and improve patient outcomes. It offers numerous benefits, from enhancing diagnostic accuracy and personalized treatment plans to streamlining administrative processes. However, it also comes with some challenges. Therefore, you need a proven vendor to solve those issues.

Integrio is a trusted provider implementing AI-powered solutions in healthcare and fitness. Our team collaborates with innovative startups, small and mid-sized companies, and well-established enterprises.

With our rich expertise and 23-year experience in the field, Integrio offers cutting-edge AI software tailored to the healthcare industry's unique requirements. Our services include cluster analysis, prediction and recommendation engines, biometric data analysis, chatbots, intelligent assistants, and more.

Contact us to create an efficient AI-powered solution for your healthcare company.


AI is used in healthcare to enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes. It analyzes large volumes of data to identify patterns and insights that aren’t readily apparent to human healthcare providers. This technology revolutionizes the industry by reducing errors, personalizing treatments, and advancing medical research and innovation.

AI can be cost-effective in healthcare since it optimizes processes, reduces errors, and improves efficiency. In particular, AI automates administrative tasks, freeing up medicians' time. Thus, they can focus more on patient care. Additionally, analyzing large datasets and providing personalized treatment plans leads to more targeted interventions. It potentially reduces healthcare costs in the long run.

Some examples include AI-powered medical imaging analysis, where algorithms assist in interpreting X-rays, CT scans, and MRIs to aid in diagnosis. AI also detects mental health disorders by analyzing facial expressions and nonverbal cues. Additionally, AI-driven chatbots and virtual assistants provide 24/7 support, answering health-related questions and offering symptom assessments.


Artificial Intelligence (AI) in Healthcare: 9 Use Cases Artificial Intelligence in Healthcare in 2023 The Challenges of Adopting AI in Healthcare Top 9 Healthcare AI Use Cases Benefits of AI in Healthcare Improve Patient Care and Operational Efficiency with Integrio FAQ

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