Generative AI in healthcare: Benefits, use case, and how to build

Learn how Generative AI is applied to transform healthcare industry, covering the benefits, common use cases, and how to build one effectively.  

McKinsey (2026) reported that half of US organizations in their survey have implemented Generative AI (Gen AI), with increasing focus on integration and ROI. According to Grand View Research, the Gen AI in healthcare market in the US is expanding quickly, driven by advanced healthcare infrastructure, strong investment in healthtech, and the widespread adoption of electronics health records (EHRs).  

In this aricle, we will discover Generative AI applications in healthcare, including the advantages, common use cases, and how to build Gen AI solutions effectively. This can provide a comprehensive guide for business leaders in implementing Gen AI effectively  

Key takeaways  

• Around 50% of US organizations surveyed by McKinset have implemented Generative AI, with more focus on AI integration and ROI.  

• The GenAI market in the US is expanding rapidly thanks to the developed healthcare infrastructure, strong investment in digital health technologies, and the adoption of EHRs.  

• The Generative AI workflow typically starts with the data preparation, request process before retrieving information and generating the outputs.  

• JAMA reported that AI scribe has decreased the total EHR and documentation time of 13.4 and 16.0 minutes, respectively. Also, it was associated with 20.4% less time in notes per appointment. 

• Thanks to LLMs and NLP, Gen AI enables patients to ask health-related questions in natural language and receive easy-to-understand explainations. 

Understanding Generative AI in healthcare  

What is Generative AI in healthcare?  

Generative AI in healthcare is the use of AI models to analyze medical data, synthesize information, and generate new content to support the healthcare quality and patient care experience.  

How it works 

Data preparation: Collect, clean, and standardize medical data, including patient records and related documents. Through this step, AI has the suitable data.  

Process requests: Receive and analyze users’ requests through technologies, such as Natural Language Processing (NLP) or Large Language Models (LLMs).  

Retrieve relevant information: Search and retrieve relevant information from database, patient records or reltaed decumentatuons.  

Generate and review the output: Use AI models to create content such as answers, summaries or suggestion.  

Genertive AI vs. Traditional AI  

Traditional AI is designed to execute a specific tasks based on predefined rules.In the healthcare sector, it is often used to predict disease risks, detecting abnormalties in medical images, or categorizing patients based on risk level.  

Generative AI: powered by LLMs, goes a step further. Besides data analysis, it can generate new content in human-like language. In the healthcare sector, Generative AI can support in summarizing medical records, preparing clinical notes, and answering health questions.  

Benefits of Generative AI in healthcare  

Automate manual work  

Generative AI can automate administrative workload such as taking clinical notes, summarizing patient records, and reporting, reducing time spent on manual tasks and increasing patient care effort. JAMA reported that AI scribe adoption, a type of Gen AI, has decreased the total EHR and documentation time of 13.4 and 16.0 minutes, respectively.  

Automatic Speech Recognition (ARS) transforms the conversation between doctor and patient into text, while NLP and LLMs define and analyze context before creating relevant information.  

Streamline healthcare workflow  

Thanks to the abilities to automate manual tasks, when integrated with clinical systems, such as EHRs ow patient monitoring management, Gen AI can reduce reputitive tasks, leading to smooth workflow. A research conducted by JAMA (2024) found that the use of Gen AI was associated with 20.4% less time in notes per appointment.  

Common technologies used:  

• Large Language Models (LLMs): Aggregate information and generate content for each task.  

• Retrieval-Augmented Generation (RAG): Retrieve relevant information from internal database.  

• API Integration: Connect AI with EHRs or existing software.  

• AI Agents: Perform multi-step tasks within granted permissions.  

Enhance personalized care  

Gen AI, powered by LLMs and RAG, can help tailor health advice, care instructions, and follow-ups based on each patient’s history, symptoms, test results, and individual needs. This  allow healthcare providers to offer more personalized guidance instead of relying solely general recommendations.  

Improve access to healthcare informtion  

Thanks to LLMs and NLP, Gen AI enables patients to ask health-related questions in natural language and receive easy-to-understand explainations of medical terms, health conditions, and care instructions. Also, AI Healthcare Assistant can provide 24/7 support outside regular clinic hours, making healthcare information more accessible.  

Accelerate medical research  

Gen AI assists staff in synthesizing scientific literature, searching for relevant evidence, formulating hypotheses, and suggesting potential drug candidates. This technology can reduce the time spent on some initial research stages, allowing research teams to focus more on hypothesis testing and outcome evaluation. 

Gen AI Healthcare use cases  

AI Healthcare Assistant  

AI Healthcare Assistant is one of the most common Gen AI healthcare use cases. It is a virtual assistant that uses Generative AI to communicate with patients or healthcare professionals through natural language. This tool can be integrated into websites, healthcare applications, or hospital systems to help users access information and perform healthcare-related tasks. 

• Answer common health questions and explain medical terminology in easy-to-understand language. 

• Scheduling appointments, sending appointment reminders, and reminding patients to follow medication schedules. 

• Compile patient information and retrieve relevant clinical guidance. 

• Guide patients to services and refer complex or urgent cases to healthcare professionals. 

Clinical documentation and summarization  

A Gen AI application that helps create, synthesize, and organize clinical documents from doctor-patient conversations, EHRs, and related medical data. Instead of manually transcribing, healthcare professionals can use AI-generated drafts to review and refine them. 

• Convert doctor-patient conversations into draft clinical notes. 

• Summarize medical history, symptoms, test results, and treatment history. 

• Prepare discharge reports, referral letters, and other clinical documents. 

• Extract and organize key information for easy review. 

AI workflow automation  

An application of Gen AI, combined with system integration technologies and AI agents, to automate repetitive tasks, supporting the execution of a sequence of tasks based on rules and access permissions. Gen AI can categorize requests, assign tasks to the correct department, and track processing status.  

Challenges  

Despite multiple benefits of gen AI applications in healthcare, there are several challenges that both healthcare firms in the US and the AI solution development company need to consider before implementing this technology.  

Data privacy and security  

AI in healthcare often needs to process sensitive data such as medical records, patient history, test results, and patient identification information. If this data is sent to third-party sides without adequate safeguards, there is a risk of data leaks, unauthorized access, or misuse. 

Also, patient data may be stored longer than expected or processed through external systems, making data control more complex. 

How to avoid: Implement measures such as data encryption, access control, anonymization or minimization of identifiable data, establish clear storage policies, and conduct security assessments of AI vendors. 

Regulatory compliance  

Gen AI applications in healthcare must meet legal requirements related to data protection, privacy, and patient safety. In the US, HIPAA regulates the protection of protected health information (PHI) within its scope of application. Also, AI applications with diagnostic or treatment decision support functions may fall under the FDA’s medical device regulation. 

The challenge lies in determining which category the AI solution belongs to, how the data can be used, and what obligations need to be met.  

Integration with existing systems 

Many hospitals are using EHR systems, hospital management software, laboratory testing, and billing systems built on different platforms. Gen AI struggles to perform effectively without access to necessary data. Data may be in inconsistent formats, lack common standards, or be scattered across multiple systems.  

Also, legacy systems may not support modern APIs or integration protocols, requiring more time and cost for deployment.  

Bias and patient safety  

Gen AI learns from available data, so if the data is inaccurate, the AI can produce biases. In healthcare, this is particularly dangerous because if doctors or patients trust that information without verifying it, it can lead to decisions that affect their health and treatment. 

How to build Generative AI in healthcare  

Step 01. Define use cases and objectives  

When building any software application, the first step is always to define the use cases and purpose. Building Gen AI is no exception. What problem will AI solve? Who will AI serve? What value will AI bring to healthcare facilities? 

• Identify the problem: Identify the difficulties in the healthcare process. 

• Define the target users: Determine who will directly use the AI solution, as each group has different needs and data access levels. 

• Choose a specific use case: Select a specific application to start with, instead of building an AI system that solves all problems at once. For example, build an AI Healthcare Assistant, clinical documentation, or workflow automation. 

• Set measurable objectives: Establish specific KPIs to evaluate effectiveness. 

• Assess clinical risk: Determine the level of impact if the AI produces incorrect results. 

The first step will give the business a clear use case, target user group, scope, KPIs, and specific requirements. 

Step 02. Data preparation  

Data quality directly impacts how well Gen AI performs. Therefore, data preparation plays an important role in building a Gen AI solution. 

• Identify relevant data sources: Determine the data sources necessary for the use case. Not every AI application needs all of these sources. Data should be selected based on intended use. 

• Obtain data access and permissions: Verify the rights to collect, use, and process data. 

• Clean and standardize data: Remove duplicate records, process missing or incorrectly formatted data, and standardize medical terms. 

• De-identify sensitive information: Remove or minimize information that could identify a patient when it is not necessary. It should be noted that removing names and case numbers does not always render the data completely anonymous as the remaining information may still allow for re-identification in some cases. 

• Organize and validate the dataset: Classify data by subject, specialty, document type, and update time. Verify the accuracy, completeness, source, and representativeness of the data. 

Step 03. Choose the right AI model and technology  

The next step is choosing how to build the AI system. Not every healthcare project requires training AI model from scratch. In many cases, using an existing model combined with clinical data is a more practical option. 

Choose a foundation model: Select a large language model (LLM) based on its ability to handle medical language, accuracy, speed, cost, deployment capabilities, and security requirements. Businesses can consider a commercial model via API or a model that can be self-deployed within a controlled infrastructure. 

Decide whether RAG or fine-tuning is needed 

• RAG is suitable when the system needs to retrieve medical documents, treatment guidelines, or internal information that can be updated frequently.  

• Fine-tuning can be useful when it is necessary to fine-tune how the model performs a task or adheres to a specific format.  

The two methods can be combined, but not every project needs both. 

Add supporting technologies: Integrate components according to the use case.  

Design the retrieval process: If using RAG, select accessible data sources, break down documents, create embeddings, establish search indexes, and rank results.  

Set generation rules and safeguards: Establish guidelines for the model regarding roles, response scope, reference usage, and how to handle information gaps.  

After this step, healthcare firms can choose the AI architecture, suitable model, and necessary components  

Step 04. Development and integration  

At this stage, AI model is integrated into a application that users can interact with.  

• Build the application interface that meets specific needs. 

• Develop the backend and AI pipeline: Build the workflow that processes requests, verifies access permissions, retrieves relevant data, sends context to the AI model, generates responses, and delivers results to users. 

• Integrate with existing healthcare systems: Connect the AI solution to EHRs, hospital management systems, or appointment scheduling platforms through APIs and healthcare data exchange standards (HL7 FHIR or HL7 v2) 

• Implement access controls: Ensure that users can only access the data and functions they are authorized to use.  

• Add human review and approval: Clearly define which actions can be performed automatically and which require human approval. 

• Build error handling and fallback mechanisms: Define how the system should respond when the model fails. The system should be able to fall back to manual workflows or forward requests to staff. 

Step 05. Test accuracy, safety, and compliance  

Before deploying Gen AI in a real healthcare environment, organizations need to evaluate not only its ability to generate responses but also its reliability, safety, and performance in unexpected situations. A model that responds quickly but produces inaccurate medical information can pose significant risks. 

What needs to be done: 

• Test factual accuracy: Prepare a set of questions and scenarios based on real use cases to evaluate the accuracy of AI-generated outputs. 

• Evaluate hallucinations and source grounding: Check whether the AI generates unsupported information, incorrect citations, or misleading interpretations of source documents. 

• Assess bias and performance across patient groups: Evaluate the model across patients with different ages, genders, languages, and health conditions. 

• Conduct privacy and security testing: Test access permissions, potential patient data exposure, the handling of sensitive information, and resistance to malicious requests. 

• Have healthcare professionals review outputs that could affect diagnosis, treatment, or patient care.  

• Review regulatory requirements: In the US, HIPAA may apply to the handling of protected health information. 

• Run pilot testing: Conduct a limited rollout with a controlled group of users and appropriate data before expanding deployment. Gather feedback from users to identify issues. 

Step 06. Deploy, monitor, and improve  

When passing the testing step, Gen AI solution can be deployed in a real environment. Also, it is not a system that can be built once and left to operate without any supervision.  

• Choose the deployment environment: Select cloud, on-premises, or hybrid deployment based on security requirements, integration capabilities, costs, and applicable regulations. 

• Roll out gradually: Start with a limited user group, department, or workflow.  

• Train users and define responsibilities: Train staff to use AI, review its outputs, and report incidents. Clearly define who approves generated content, who manages the system, and who handles inaccurate outputs. 

• Monitor performance using KPIs: Track metrics such as accuracy, the percentage of outputs requiring corrections, response time, cost per task, user adoption, and user satisfaction.  

• Track incidents and user feedback: Record cases involving inaccurate content, missing information, irrelevant sources, or workflow disruptions. Analyze the causes and found out how to fix.  

• Update data and system components: Update medical references when guidelines change, remove outdated sources, and adjust configurations when necessary.  

Building Generative AI solutions for healthcare with Adamo Software  

Founded in 2018, Adamo Software is an AI and software development company based in Vietnam. We deliver a wide range of AI solutions, from custom AI & Machine Learning, Gen AI & LLM, Computer Vision & NLP Development, and AI integration.  

Our Generative AI solutions are built on large language models, from RAG systems to fine-tuned domain models. Our LLM applications are designed beyond chatbots, including document processing, content generation, and knowledge management.  

What’s included:  

• RAG system design and implementation 

• LLM application development (OpenAI, Anthropic Claude, open-source models) 

• Prompt engineering and evaluation frameworks 

• Vector database integration and retrieval optimization 

Unlock full potential of Generative AI in healthcare sector with us.