22 Applications of Gen AI in Healthcare

Updated on February 8, 2024

This article will explore the applications of Generative AI (Gen AI) in healthcare, from administrative operations to patient care. 

Gen AI is rapidly evolving from a trending topic to a table-stakes component in various business operations. Its standard applications span text, image, and audio generation, addressing diverse business needs such as customer support, smart search in knowledge bases, content summarization and generation, and software development. 

Recent research by McKinsey & Company revealed that about 30% of organizations surveyed are already incorporating Gen AI into their operations and 40% of organizations plan to increase their overall AI investment. 

Despite these promising statistics and the growing adoption of Gen AI, many healthcare organizations are yet to fully harness its potential. In this article, we outline the specific areas in healthcare where the use of Gen AI brings clear value. 

Operations and Administrative Assistance

This section outlines the myriad ways in which Gen AI is advancing the support systems within healthcare facilities, including customer support, knowledge management, data extraction, and patient communication. From interactive helpdesks that provide immediate assistance to patients, to intelligent knowledge bases that keep medical professionals informed and up-to-date, Gen AI is streamlining the way administrative tasks are managed, reducing workload and optimizing the patient care experience.

1. Interactive Helpdesks

Gen AI can bring marked improvements to customer support. DataArt’s virtual support agent PoC, for instance, autonomously handles routine inquiries, such as scheduling appointments, providing information on insurance coverage, and providing guidance on the use of medications. The solution is continually learning and becoming more accurate and efficient over time. It is multi-lingual, scalable and cost-effective, allowing healthcare providers to manage fluctuating volumes of inquiries without hiring extra staff, or compromising support quality. 

2. Knowledge Bases

Gen AI is revamping knowledge management within healthcare systems. It transforms internal databases of learning materials, how-tos, and instructions into intelligently indexed knowledge bases, ensuring they remain navigable and relevant as they age and grow. Advanced algorithms can be used to organize educational content, enabling flexible search capabilities and personalized recommendations. Smart indexes allow for the compilation of bespoke learning paths tailored to individual employee needs. This approach to information curation ensures that medical professionals have swift access to essential data and educational resources, which is critical for both ongoing professional development and enhancing patient care outcomes.

3. Data and Insights Extraction 

Healthcare professionals often find the process of extracting specific data or insights from a range of healthcare information systems cumbersome and inefficient, from deciphering structured and unstructured data such as patient notes and scanned documents in electronic health records to gaining insights from patient monitoring devices data, lab test, or imaging. Gen AI is the perfect technology to address this problem, as it excels at processing large, diverse and complex datasets and at providing specific answers or requested data points, thus reducing the risk of important information being missed, and increasing the accuracy of clinicians’ decision-making process. 

4.   Generating Medical Documentation 

Gen AI technology can be used to capture doctor-patient interactions and generate comprehensive summaries, medical notes, prescriptions and visit schedules. This helps in accurate record-keeping and significantly reduces the time medical personnel spend on documentation.

5.     Personalizing Patient Communications 

Gen AI can help personalize outbound communications such as emails, texts, and push notifications, tailoring it to patients’ conditions, provider’s recommendations, and scheduled events.

7.    Generating Synthetic Healthcare Data 

Gen AI is invaluable for generating synthetic data for software development and testing, closely mimicking real-world scenarios while safeguarding patient privacy and adhering to privacy regulations.

8.    Creating Marketing Content 

Gen AI can power the creation of personalized marketing content, such as emails or social media posts, tailored to individual patient or doctor needs and interests. It can also facilitate targeted advertising campaigns and generate images, contributing to more effective patient engagement strategies.

MicrosoftTeams image 55 copy

Provider Solutions

In this segment, we delve into the impact of Gen AI on the delivery of healthcare services, from supporting patient visits with comprehensive history and screening preparations to generating detailed medical reports. This section also covers virtual health assistance, predictive analytics, treatment recommendations, and the enhancement of medical imaging. 

  • Medical Reports Generation

Addressing the challenges faced by doctors who must contend with patient data scattered across multiple platforms and formats, DataArt developed Doctor’s Copilot. This tool targets the inefficiencies in managing and synthesizing information from various sources such as handwritten notes, digital records, audio recordings, and image files. By aggregating and transforming this data into a cohesive text format, the Copilot helps physicians compile comprehensive medical reports and quick data analysis.

  • Patient Visit Assistance

For initial patient visits, Gen AI can synthesize various documentation sources, including audio and notes, into structured reports. This helps physicians build a thorough understanding of the patient’s medical history, improving the efficiency and quality of the consultation. 

  • Virtual Health Assistance

A human-like chatbot can be a great help in bridging the gap between the demanding schedules of doctors and the patient’s need for assistance throughout their treatment journey. Such chatbot can support patients with visit- and treatment-related queries, assist in scheduling, provide reminders, and help prepare for medical procedures. It can also address some health-related questions, while leaving treatment decisions to the doctor.

  • Treatment Recommendations 

Gen AI holds immense potential in creating personalized treatment recommendations by correlating medical ontologies and databases, with individual medical histories, genetic makeup, and other relevant factors. 

  • MRI and CT Images Improvement

Traditional imaging techniques often yield images that are less than optimal in clarity and detail. Gen AI addresses this challenge by learning from large datasets of medical images and generating improved, high-resolution versions of the original images. This advancement is crucial in radiology, where the quality of an image can significantly impact the accuracy of diagnoses.

Digital Health and Patient Experience:

This section will explore how Gen AI can make healthcare more interactive, responsive, and personalized. We examine applications of the technology across various functions, from crafting educational materials that explain health conditions to providing AI-driven primary care that’s available anytime, to mental health support, cognitive assessment and enhancement, all tailored to meet the unique needs of each individual’s health journey.

  • Autogeneration of Educational Materials: This involves creating age-appropriate and engaging content to help patients understand their health conditions. For instance, a platform could produce interactive storybooks or games that teach children about managing diabetes, recognizing the importance of diet and exercise, and explaining how to monitor their blood sugar levels. Such materials are designed to be accessible and understandable, often using animation and relatable characters to explain complex concepts.
  • Virtual Primary Care: Chatbots and avatars can serve as the first point of contact for patients seeking health advice. These virtual assistants use AI to guide patients through symptom checkers, provide health education, and offer advice on whether the situation requires an in-person doctor visit. For example, a chatbot might help a patient with a mild fever and cough determine if they should rest at home, visit a pharmacy, or visit a doctor. 
  • Scheduling Assistant Chatbots: These AI-driven tools simplify the process of finding the right healthcare provider and booking appointments. They can interact with patients to understand their symptoms, access doctors’ calendars to find available slots, and even provide reminders for upcoming appointments. For example, a patient with knee pain might interact with a chatbot that determines the need for an orthopedic specialist and schedules an appointment with the next available doctor, while also answering questions about visit preparation.
  • Mental Health Solutions: These include meditation aids, emotional diaries, and risk factor identification tools. 
  • Self-Assessment Tool for Cognitive Disorders: Gen AI can be trained to analyze facial changes and correlate that data with the results of a guided self-assessment while considering the identified risk factors. By doing so, such solutions aim to provide early detection and better monitoring of cognitive disorders. Its ultimate goal is to enhance the accuracy of diagnoses and enable timely interventions, potentially improving treatment outcomes and quality of life for individuals with cognitive impairments.
  • Cognitive Training Tools and Games: DataArt developed mage generation software utilizing Gen AI that is used for stimulating patient brain activity for patients with cognitive disabilities.
  • Tailored Care Pathways: Personalized recommendations and treatment plans for diseases and chronic care management can be derived from an analysis of unstructured patient data, such as medical histories and test results.
  • Personalized Treatment Assistants: Such solutions can accompany patients on their treatment journey by analyzing screening results and treatment efficacy. This technology can offer tailored recommendations, though it’s essential to integrate ethical considerations and professional oversight.

Next Steps for Adopting Gen in Healthcare:

If you’re considering adoption of Generative AI technologies to enhance your healthcare organization’s efficiency and capabilities, follow the steps outlined below. Also, consider partnering with a software engineering company to accelerate and derisk the development and deployment process.

  • Prioritization: Identify the most critical use cases for Gen AI within your healthcare organization based on relevance, potential impact, and feasibility.
  • Alignment and Buy-In: Secure consensus among stakeholders regarding expected outcomes and the parameters of the project, ensuring management buy-in. 
  • Data Readiness: Decide on what data should be used and how it will be pre-processed and used in the LLM.
  • Talent Selection: Build a team of experts who will drive the project and support the rollout.
  • Tool Selection: Choose the right tools that comply with healthcare regulations, focusing on patient data privacy and security.
  • Prototyping: Develop a prototype to test the feasibility of Gen AI solutions in a controlled environment, allowing for iterative improvements.
  • Evaluation: Evaluate the prototype’s performance, including its effectiveness, user experience, and impact on healthcare outcomes.
  • Roll-out: Implement the Gen AI solution in a phased approach, scaling from pilot programs to wider deployment across the organization.
  • Governance: Establish a framework for ongoing supervision, model re-training, and system support to ensure sustainable operation.


Generative AI can advance almost every healthcare-related business in numerous ways. From administrative enhancements to diagnostic accuracy, treatment personalization and innovative patient care solutions, Gen AI is poised to become an essential element in healthcare operations, bringing about new levels of efficiency and a more patient-centric approach to care. This integration promises to improve how healthcare services are delivered and managed, aligning closer with patient needs and healthcare provider capabilities.

Olga Romanova
Olga Romanova
Engagement Manager, Healthcare & Life Sciences at DataArt

Olga Romanova is Engagement Manager, Healthcare & Life Sciences at DataArt.