Contact us
Our team would love to hear from you.
Back to blog 
Generative AI in healthcare: benefits and top use cases
Generative AI is rapidly changing the healthcare industry, providing new, more efficient approaches to both clinical and administrative tasks. Although the adoption of generative AI among healthcare executives is still cautious, with most organizations piloting the technology in limited use cases, interest and experimentations continue to rise each year.
In this article, we will assess the state of generative AI in the healthcare market, examine use cases of this technology for healthcare organizations, outline its benefits and challenges, and identify best practices for implementation.
Generative AI in healthcare industry
Generative AI has the potential to reshape healthcare, providing new opportunities for medical institutions, practitioners, and patients. Approximately 81% of healthcare executives view generative AI as a critical technology capable of transforming the industry. Considering the current high adoption rates, generative AI in healthcare is expected to reach USD 39.7billion by 2034, up from just USD 2.64 billion in 2025.
Source: precedenceresearch.com
Still, many organizations face barriers to generative AI implementation, including lack of tool maturity, financial risks, and compliance concerns.
Source: academic.oup.com
Applications for generative AI in healthcare
Generative AI is reshaping approaches to a wide range of healthcare tasks and challenges, from medical training to personalized patient care. Let’s explore some common generative AI in healthcare examples.
Automation of administrative tasks
Generative AI has proven to be highly effective for administrative tasks. In a McKinsey survey, about 75% of respondents said it has the greatest potential to improve administrative efficiency.
Patient communication
Chatbots powered by generative AI can provide responses to patient queries, streamlining administrative operations and allowing medical staff to focus on other tasks. Generative AI can also improve machine translation, fostering not only patient communication but also knowledge exchange and global collaboration in medical education.
Schedule optimization
Generative AI can analyze a medical staff’s current workloads and suggest schedule optimizations, leading to more efficient workflows. It can also automate the process of booking and rescheduling patient appointments, optimizing time management for healthcare practitioners.
Record keeping
Medical personnel can use generative AI to transcribe patient sessions and fill in electronic health records (EHRs) and other clinical documentation, freeing time for other tasks. The technology can then provide summaries and actionable insights based on these records.
Report generation
Generative AI models can create comprehensive structured health reports on medical services, specific conditions, patients, clinical findings, test results, and more. The technology can also be used to generate financial, legal, and compliance reports.
Other administrative functions
To function effectively, healthcare facilities depend on numerous nonmedical processes and departments, including recruitment, procurement, IT, and finance. Generative AI in healthcare can assist these teams by creating offer letters, customizing onboarding, drafting contracts, creating purchase orders, developing code, running cybersecurity tests, and more.
See also:
Healthcare data analytics
EffectiveSoft provides healthcare analytics services to hospitals, public health organizations, clinics, medical device manufacturers, and other healthcare businesses. We create competitive data analytics solutions that empower medical facilities to extract profound insights and make efficient clinical decisions. The outcome? Streamlined operations, optimized costs, and enhanced value-based care.
Medical education and training
The use of AI in healthcare practical training solutions helps medical students and practitioners develop and perfect essential skills, offering them realistic experiences comparable to real-world scenarios.
Simulation-based learning
Using generative AI along with augmented reality (AR) or virtual reality (VR), medical educational institutions and healthcare providers can mimic real-life scenarios and environments in a safe and controlled setting. Simulation-based learning allows students to apply theoretical concepts in practice, using a trial-and-error process to acquire knowledge and hands-on skills and gain confidence in those skills. Through simulations, medical students and practitioners can examine difficult or unusual cases and prepare for operations. Generative AI in healthcare is also useful in providing instant feedback and assessing learners’ performance, helping them identify difficult areas requiring more attention.
Patient education
Generative AI can create personalized educational content for patients, taking into account their conditions, treatment history, and other relevant data. The technology can generate useful information at different reading levels to accommodate various degrees of knowledge, education, and comprehension, improving accessibility for all users.
Medical data analysis and generation
Medical data analysis and generation play crucial roles in medical decision-making and research aimed at understanding human diseases and identifying new diagnostic methods, prevention strategies, and treatments to improve overall human well-being. Generative AI is emerging as a valuable tool in these areas. Here are several examples of generative AI in healthcare data analysis and generation.
Data extraction and analysis
Generative AI enables natural language processing (NLP) models to comprehend and interpret the context of processed text. This capability facilitates the automated extraction and analysis of data from medical literature, documents, and patient records. For instance, it can be integrated into systems that automatically summarize medical documents and records, uncovering valuable insights for further research and streamlining the decision-making process.
Generation of synthetic medical data
The privacy and security of sensitive health data is one of the main concerns in medical research. Synthetic clinical data resembles actual patient information and can be used in research without compromising privacy. Generative AI, particularly generative adversarial networks (GANs), can be trained to create realistic synthetic medical data incorporating demographics, medical conditions, treatment histories, individual characteristics, and various medical scenarios, thereby improving and accelerating clinical trials and research.
Drug discovery
Generative AI can support drug discovery by analyzing large chemical and biological datasets to predict how different compounds might behave, identify promising drug candidates, and estimate their likelihood of being safe and effective, helping researchers narrow down options faster.
AI solutions for healthcare
See what we offerPersonalized patient care
Generative AI in healthcare can facilitate personalized treatment approaches. The technology can provide tailored solutions for medical treatment and provide answers to patient questions.
Disease diagnosis
Generative AI models can be trained on EHRs, medical images, clinical measurements, and expert annotations to detect patterns, anomalies, and signs of disease. They can process, understand, and summarize information, creating a holistic overview of a patient’s condition. For healthcare organizations, this translates to improved clinical decision-making when assessing complex patient cases.
Personalized medicine
Generative AI applications in healthcare can help create targeted treatment plans for unique cases. By analyzing patient data, individual genetic and clinical profiles, historical data, and other medical records, generative AI models help medical professionals develop personalized medicine plans, improving patient outcomes.
Virtual health assistants
Powered by generative AI, virtual assistants support patients through conversational, personalized guidance, answering common questions, explaining care instructions, and helping with tasks like scheduling and medication reminders. Their responses are constrained to approved clinical guidelines, protocols, and curated knowledge bases. Generative AI assistants do not make diagnoses or provide treatment and escalate clinical decisions, urgent symptoms, or uncertainty to licensed healthcare professionals.
Benefits of generative artificial intelligence
Generative AI for healthcare is expected to completely transform the industry, helping medical personnel perform their jobs more effectively and streamlining processes. Following are some of the key benefits of generative AI that healthcare companies can leverage.
Overcoming data limitations
Traditional medical research and treatment is limited by the amount of information humans can perceive. Generative AI in healthcare is capable of analyzing and learning from vast amounts of historical and current data to produce actionable insights for researchers and practitioners, leading to better outcomes.-
Reducing human error and burnout
Solutions powered by generative AI in healthcare reduce human error through comprehensive analysis and assistance with diagnoses, treatment plans, and outcome predictions. These solutions are also capable of tackling repetitive tasks that burden medical professionals, reducing burnout. -
Improving efficiency
Generative AI can be used to automate repetitive and routine tasks, streamlining processes and making workflows more efficient, including assisting in drafting clinical notes and patient communications. This alleviates the documentation burden and saves time for healthcare personnel, allowing them to focus on tasks that require more attention and reducing burnout. -
Enhancing patient experience
Patients can get quick answers to their questions and promptly deal with administrative issues via virtual assistants and chatbots, receive notifications about required medications and procedures, and schedule and reschedule appointments. Generative AI in healthcare automates certain clinical and operational tasks and enables more accurate diagnoses and personalized treatment plans, increasing patient satisfaction and loyalty.
Challenges of implementing generative AI in healthcare
Despite all the advantages of implementing generative AI in healthcare, this technology does come with some risks. Greater use of generative AI in clinical decision-making makes accountability, regulation, and transparency increasingly important.
Use-case fit
Before implementing a generative AI in healthcare, it is essential to evaluate its clinical utility. This assessment is crucial for the deployment of an AI solution and involves determining if the problem being addressed is worth solving, as well as which model to use. In pursuit of accuracy, healthcare companies might choose a model with no clinical utility. However, a more useful model could be less accurate. In such cases, decision curve analysis can identify the most applicable and beneficial generative AI model for the specific healthcare application.
Data privacy and compliance
Generative AI models are trained on large amounts of data, including personal health information and other sensitive data, which puts this information at risk. To avoid data breaches and ensure the safety of patient information, healthcare institutions must comply with data protection regulations and follow industry standards, such as HIPAA and GDPR. Regulatory bodies are also working on how best to oversee adaptive, learning-based AI systems, especially those used to support diagnosis or treatment decisions.
System integration
Implementing generative AI in healthcare systems is a key component of the digital transformation process that many medical organizations are pursuing. However, numerous companies still have legacy systems that are incompatible with advanced technologies. Before integrating generative AI into their processes, healthcare organizations must assess the current state of their IT infrastructure and ensure it can accommodate new generative AI tools. It is also essential to cleanse and standardize data so generative AI models will work properly and generate relevant output.
Potential bias
Generative AI model outputs rely heavily on the quality of the training data. If the training data is biased or inaccurately represents the population, the results will also lack diversity and inclusion. To address this challenge, healthcare organizations should incorporate data processing and validation techniques to detect and correct bias and errors, and continuously monitor system outputs.
Fault results
Despite the accuracy that generative AI can offer, the technology is still evolving and is not immune to errors. There is also the risk of AI hallucinations, where a generative AI model produces nonsensical or misleading outputs due to incorrect assumptions, insufficient data, or biases in the data. To mitigate this risk, human review of AI-generated outputs is essential. Enhancing transparency in the development and deployment of AI solutions, including the data they are trained on and the methods they use to generate outputs, can also help address this issue.
Feasibility
The feasibility of a generative AI model relies on many aspects, including data quality and availability, deployment challenges, and maintenance. It is important to use relevant data, ensure representation, and make data processing transparent. A generative AI solution must be valid and generate meaningful outputs. However, this can be difficult to evaluate because healthcare companies often rely on their own experience or that of their colleagues.
See also:
Streamlining patient care with NLP
We helped bridge the healthcare data gaps with AI and NLP integration.
Where to start when integrating generative AI in healthcare
The best practices for integrating generative AI in healthcare are based on the challenges this technology presents.
Determine the need
Changing processes and workflows in the healthcare industry is not easy. Organizations must first assess their requirements and determine how advanced technologies like generative AI can best serve them.
Key questions to consider include:
- What procedures and processes do you want to optimize and automate?
- What use cases are relevant?
- What value should the new tools bring?
- What technological capabilities are currently available?
What about you?
Knowing where to start is the hardest part. Our AI workshops help healthcare organizations cut through the noise, map opportunities, and build a practical path forward. Talk to our expert team.
Assess risks
Another important step is to evaluate all the risks and challenges associated with integrating generative AI in healthcare processes and develop a plan to address them. This includes assessing the IT infrastructure and the readiness of the personnel to adopt this technology. Healthcare organizations should prepare their technological resources and ensure they can effectively leverage generative AI models
Collect and prepare the data
The datasets used to train generative AI models directly impact the results they generate. Accurate, high-quality input data is essential for obtaining valuable outcomes and minimizing the risk of faulty or harmful outputs. To ensure the fidelity, quality, and security of the data, healthcare organizations should consider partnering with technology experts to assist with data preparation.
Engage people
When integrating generative AI solutions into a healthcare organization’s workflows, a key concern is how personnel will interact with the technology. Providing educational support and establishing guidelines for using these new tools is essential. Generative AI in healthcare should improve efficiency and facilitate work, not create barriers for employees.
An experienced technology partner is crucial for the successful implementation of a generative AI solution. The potential vendor should comply with healthcare industry standards and local regulations and have domain expertise to deliver a comprehensive and reliable product.
Generative AI development
Explore our expertiseConclusion
The generative AI use cases in healthcare demonstrate its importance, transformative impact on the industry, and potential to advance and streamline processes such as diagnosis and patient treatment. Despite the challenges, generative AI solutions are designed to improve healthcare outcomes. To learn more about generative AI and take the first step toward adopting this new technology, contact our team.
F.A.Q. about generative AI in healthcare
Generative AI is a technology that can learn from existing data to produce new, high-quality content. Generative AI in healthcare trained on medical record data can facilitate various processes, including diagnosis, administrative procedures, patient interaction, and more.
The key difference between the two types of AI is their capabilities and use cases. Traditional AI is used for data analysis and forecasting. Generative AI creates new data based on training data.
The cost of a generative AI solution depends on many factors, including the type of model, the tasks it will perform, the implementation approach, customization options, and more. Contact our team, share your idea, and receive a project estimate tailored to your needs.
The timelines for generative AI software development in healthcare depend, among other things, on risk level, data access, and integration path. For example, a low-risk, non-clinical workflow helper takes about 6-12 weeks to release, but a more sophisticated solution, like clinical decision support, takes over 18 months to develop. Contact us for a tailored project estimate.
Generative AI-specific applications include clinical note generation, summarization, patient-facing content, coding and billing support, synthetic data generation, and workflow automation.
The best starting point for generative AI are usually low-risk, high-volume workflows, such as administrative drafting (summaries, letters, forms), patient messaging draft with human review, and documentation support (notes). These generative AI use cases in healthcare deliver value without making autonomous clinical decisions.
When using generative AI for administrative drafting, sampling-based review may be enough. For use cases that directly influence care, documentation in the electronic health records, or patient guidance, review should be consistent and explicit.
Yes, generative AI can be used for disease diagnosis and personalized medicine, but mostly as an assistant. It can help clinicians by summarizing patient history, drafting differential diagnoses for review, and suggesting guideline-based next steps. For personalized medicine, generative AI can support risk categorizing, treatment planning discussions, and report interpretation when used with clinical oversight.
Commonly, generative AI model families are grouped into transformer-based models that are used for text generation (LLMs) and power chatbots and clinical note drafting; generative adversarial networks (GANs) that generate realistic synthetic data; variational autoencoders that learn compact representations and generate new samples; and diffusion models that are used for high-quality image generation.
Top seven examples of AI in healthcare are medical imaging analysis, clinical documentation support, clinical decision support, patient triage and symptom intake, administrative operations automation, remote monitoring and prediction, and drug discovery and trial support. AI is also increasingly used in mental health through early screening, symptom monitoring, and care navigation.
Generative AI in healthcare is moving from experimental, niche applications to full-scale integrated adoption, focusing on care personalization, administrative workflow automation, and clinician augmentation. Key application areas will include more reliable documentation integrated into EHRs, personalized patient communication at scale, stronger governance, and more multimodal AI. However, ethical considerations and challenges like safety, data privacy, accuracy and hallucinations, and regulatory compliance remain.
Generative AI is not automatically HIPPA compliant. HIPAA compliance depends on how the tool is deployed and governed, including data handling. An experienced software development company ensures a solution is compliant end-to-end.
Yes, generative AI is used for some cases in the US healthcare system, such as administrative and clinician-workflow support and in some places even for clinical decision support under strict oversight. Adoption of generative AI is fastest where it brings real value, i.e., saves time, reduces burnout, and improves outputs.
Current applications of AI in healthcare commonly include imaging and diagnostics support, risk prediction, clinical documentation and summarization, operational efficiency, and revenue cycle.
STILL HAVE QUESTIONS?
Can’t find the answer you are looking for?
Contact us and we will get in touch with you shortly.
Blog
Related articles
Why U.S. companies choose AI development in Costa Rica
Artificial intelligence (AI) has transitioned from mostly experimental use cases to a core operational capability for U.S. enterprises. This is why scaling AI is now central to business operations. However, U.S. companies face talent shortages, high infrastructure costs, complex compliance rules, and the need for continuous delivery of production-ready AI systems.
Practical applications of AI communication protocols (MCP, ACP, A2A, ANP)
In multi-agent systems and artificial intelligence (AI) applications, individual agents face a fundamental limitation: they cannot achieve complex goals in isolation. Without standardized communication mechanisms, agents remain trapped in silos, unable to coordinate with one another or use external tools effectively. Communication protocols solve this critical bottleneck by establishing the rules and interfaces that enable agents to share information, negotiate tasks, and access external resources.
AI-powered legacy code modernization and migration: modernize your tech stack, improve performance, and future-proof your systems with AI-driven efficiency
Legacy code is expensive to maintain, distracts resources, hinders innovation, and exposes your business to security risks. Traditional system re-writes demand significant time, cost, and human resources, and often fail to meet expectations. So, how can you modernize systems without the complexities of traditional reengineering? AI-augmented legacy system modernization allows organizations to upgrade systems more quickly and safely, reducing costs and unlocking innovation.
Revenue cycle management workflow automation
Revenue cycle management (RCM) is the foundation of healthcare finance, ensuring that organizations receive payment for the care they provide. Due to its complexity, this multistep process is prone to errors and inefficiencies, leading to delays in reimbursements and increased costs. RCM automation significantly reduces denial rates, eases administrative burdens, and improves the financial experience for both providers and patients.
AI-powered medical coding
Fast and accurate medical coding is essential for healthy revenue cycle management (RCM). Yet this process is prone to costly errors, prompting healthcare organizations to seek more effective, smarter methods. That’s where advanced technology like artificial intelligence (AI) comes in. When precision and speed matter, AI is the ultimate answer.
Denial management in medical billing
Revenue cycle management (RCM) is a foundational process in healthcare administration that handles the financial side of patient care. Denial management is a critical component of RCM that plays a direct role in maintaining a healthcare organization’s financial stability. If effective, it ensures healthcare providers are paid for their services accurately and on time, improves revenue recovery, and strengthens payer–provider relations. However, for many healthcare organizations, achieving efficient denial management remains a challenge.
General agentic design patterns implemented on OCI
Artificial intelligence (AI) agents have taken the business world by storm, and for good reason. These smart assistants plan ahead, fulfill tasks with various degrees of autonomy, and make sensible decisions on their own. If deployed and managed in the cloud, agentic systems effectively execute resource-intensive jobs and scale dynamically as business demand grows. Oracle Cloud Infrastructure (OCI) is the ideal platform to implement robust agentic design patterns that support highly functional and secure solutions.
AI agents: capabilities and use cases
As businesses increasingly adopt generative AI (GenAI), AI agents represent the next frontier, redefining how autonomous problems are solved. With the ability to reason, act, learn, and make decisions independently, AI agents offer businesses across various domains unprecedented efficiency and productivity. The question is no longer if you should embrace agentic AI, but how quickly you can integrate it to stay ahead.
Medical claim management: how AI is solving healthcare’s multibillion-dollar problem
Healthcare claim management has traditionally been ineffective: slow, error-prone, and costly. Now, artificial intelligence (AI) is fixing the issues. With smart automation, predictive analytics, and intelligent software tools, AI is driving faster reimbursement, reducing mistakes, and improving financial health of care delivery.
Top 7 frameworks for building AI agents: which one fits your use case?
Every company wants its workflows to be smarter and more efficient, and AI agents are the ideal solution. These intelligent systems act like loyal employees that operate around the clock, orchestrate a myriad of unrelated workflows simultaneously, and solve big-picture problems independently. But what if building a ground-up AI agent is too costly for your business? AI agent frameworks offer more cost-efficient and time-saving options.
Generative AI consulting company: does technology advisory make a difference?
Modern businesses have discerned the true value of generative AI: the democratization of art, automated workflows, and substantial cost savings. While some companies rush to adopt the technology no matter the risks, others stay on the sidelines, discouraged by challenges. So, how do you integrate generative AI safely and effectively, turning it into a powerful tool rather than a risky liability for your business? Generative AI strategy consulting is the answer.
LLM сomparison: how to choose the right model for your business
Major companies like Spotify, Salesforce, and Duolingo are already using large language models (LLMs) to improve podcast recommendations, facilitate financial analytics, and deliver hyper-personalized experiences to global users. Following their lead, more businesses have begun integrating LLMs into their operations. But with an impressive list of large language models, choosing the right one becomes a challenge.
A new milestone achieved: EffectiveSoft expands its talent pool of Certified Azure AI Engineers
Certification plays a key role in specialists’ career growth and development. However, EffectiveSoft recognizes that supporting its employees in obtaining prestigious designations and staying current with disruptive technology trends isn’t just about building skills internally—it’s about prioritizing clients. Every upskilling initiative revolves around our clients’ unique business needs and unresolved challenges.