Did you know that 62% of a doctor’s time is spent on electronic medical records and not with the patient? And this is only one issue out of hundreds that gen AI and LLMs in healthcare could help solve.
However, LLMs use cases in healthcare, as well as generative AI for healthcare, are no longer a distant prospect – they’re slowly becoming an integral part of clinical workflows and innovation strategies. According to Statista, in 2024 alone, one in five clinicians worldwide reported using ChatGPT in their work, highlighting growing interest in AI-assisted decision-making, documentation, and patient communication.At the same time, investments demonstrate strong confidence in AI as 42% of global digital health funding went to AI-focused companies, meaning that both startups and established players view generative AI as a major driver of the next wave of healthcare innovation. So, in today’s article, we will try to answer the question: how is generative AI being used in the healthcare industry, what are its capabilities, and what are its limitations? Let’s begin!
What is generative AI, and how is it going to impact healthcare?
Before we explore the capabilities and limitations of generative AI in healthcare, let’s understand the concept of generative AI and its role in healthcare.
In simple terms, generative AI software development in healthcare involves building computer programs that can automatically create valuable content, such as medical notes, patient letters, summaries of lengthy records, or reminders. Instead of doctors spending hours writing and organizing paperwork, the AI does it for them, saving time and allowing healthcare professionals to focus more on patient care. Medical LLMs, specialized large language models trained on clinical data, enable healthcare providers to draft notes, summarize patient records, and support clinical decision-making more efficiently.
For example, smart CRM systems with AI chatbots are a practical example of AI in action. These tools keep patients connected between visits, provide personalized post-care guidance, send automated reminders, and handle routine administrative tasks. By reducing missed appointments, improving adherence to treatment plans, and freeing staff from repetitive work, AI helps clinics operate more efficiently and reduce physician burnout, as well as enhancing patient satisfaction and safety. Thus, with gen AI, healthcare providers can shift more focus back to patient care, improve outcomes, and build a more sustainable, patient-centric healthcare system.
Capabilities of LLMs and Generative AI in healthcare
Below, we will discuss how healthcare LLMs and generative AI can revolutionize the healthcare industry, from streamlining workflow and accelerating processes to enhancing patient care, education, and training. So, how AI can be used in healthcare?
Efficiency and time savings with gen AI
Medical staff are often occupied with patient consultations, diagnostic procedures, treatment planning, and administrative tasks. AI-driven solutions can help healthcare providers work more efficiently and automate routine tasks. For example:
Streamlined workflow
AI can streamline clinical workflow by integrating with healthcare IT infrastructure. This includes interoperability solutions, smart electronic health record (EHR) interfaces, and workflow automation tools. In this case, AI effectively reduces the need for manual data entry, improving overall productivity within healthcare settings. Through automation and intelligent processing, AI systems optimize the flow of information, allowing healthcare professionals to focus more on patient care rather than administrative tasks.
A good example of gen AI use cases in healthcare is Kitrum’s Custom Healthcare CRM, which helps clinics reduce their administrative workload by up to 40% through the automation of scheduling, billing, insurance management, and patient communication. By centralizing patient data, the system eliminates costly duplication, streamlines claims processing, and reduces no-shows by nearly 30%. Its AI-powered analytics turn complex datasets into actionable insights, enabling better decision-making and improving both efficiency and patient outcomes.
By deploying a ready-to-customize CRM in as little as 3–6 weeks, healthcare providers can save up to 12 months of development time while strengthening security, reducing burnout, and ensuring a smoother patient-first workflow.
Saving time
Healthcare providers spend a lot of time and resources manually preparing prior authorization requests for insurance companies for medical procedures. What if gen AI did this instead?
For instance, Doximity GPT offers a solution that automates the generation of prior authorization letters. Healthcare providers can input patient and procedure information into the platform, and Doximity quickly generates detailed letters tailored to the specific procedure and patient, including clinical justifications and references. This streamlines the prior authorization process, saving healthcare providers valuable time and resources and ensuring timely patient access to necessary medical procedures.
But AI can go even further. Autonomous AI agents optimize your business process routines, handling entire workflows. Instead of just producing letters, these agents can manage the full cycle of prior authorization requests – from preparing documentation to submitting it, tracking responses, and flagging cases that need human review. By automating these end-to-end processes, autonomous AI agents reduce administrative burdens, minimize delays, and enable clinicians to spend more time with patients.
Reducing errors
This streamlined approach not only saves time but also minimizes the risk of errors associated with manual data entry, thereby improving the efficiency and effectiveness of healthcare delivery. However, it’s important to always double-check the documentation that AI wrote.
Navigating document complexity
Physicians often struggle to efficiently navigate and comprehend extensive medical policies and insurance documentation. With documents spanning hundreds of pages and filled with intricate details, finding specific information quickly becomes daunting, leading to delays in patient care, potential errors, and increased administrative burden on healthcare providers.
Here’s where Kitrum’s upcoming AI-powered document parser can make a difference. It is designed to handle even the messiest, multi-page reports filled with inconsistent tables, scattered numbers, and mixed text. Kitrum’s upcoming AI-powered document parser extracts key data accurately and converts it into a structured, ready-to-use format. In testing, the parser achieved error rates below 1% and processed complex reports several times faster than generic tools, eliminating the need for manual cleanup.
Optimized resource allocation
AI optimizes resource allocation in healthcare by analyzing data to forecast patient demand and allocate staff efficiently. Automated scheduling systems create fair schedules, saving time and minimizing errors. This prevents understaffing, reduces overwork, and improves work-life balance for medical staff.
On the other hand, optimization of resource allocation can look like the next. Imagine a patient arriving at a doctor’s office with overwhelming background documentation. Manually reading and analyzing this extensive medical record would be time-consuming for the doctor. However, by leveraging AI technology, the doctor could quickly and accurately summarize the patient’s current state or extract specific details, which he would need hours and hours to look for without the help of AI.
Fraud prevention
Fraud prevention has become a paramount concern in the healthcare industry, with the Global Healthcare Fraud Analytics Market poised for significant growth in the coming years. According to a recent report, the market is projected to soar to approximately USD 20.4 billion by 2032, marking a substantial increase from USD 2.5 billion in 2022. This remarkable expansion is attributed to a compound annual growth rate (CAGR) of 23.5%, expected between 2023 and 2032. As healthcare fraud continues to pose severe financial and reputational risks, adopting advanced analytics solutions is essential for detecting and preventing fraudulent activities and safeguarding the integrity of healthcare systems worldwide.
Gen AI delivers a patient-centric approach
AI technologies can be used to prioritize patients’ needs and experiences by personalizing treatment plans, improving communication between patients and healthcare providers, or enhancing the overall healthcare experience. Let’s see how it’s possible:
Virtual medical assistance
We have all complained about long queues at the doctor’s office at least once. Thus, many people have started to use virtual medical aid. According to a Rock Health report, 80% of respondents reported accessing care online at least once, marking an increase of 8% points from 2021.
AI virtual nurse assistants, which include medical large language models, applications, or similar interfaces, provide patients with convenient remote access to healthcare support, offering timely assistance with medication management, symptom monitoring, and forwarding reports to healthcare providers or surgeons, as well as facilitating patient appointments with physicians. An LLM healthcare chatbot leverages advanced language models to provide patients with accurate, conversational support, answer medical questions, and assist clinicians with administrative tasks.
And all from the comfort of patients’ homes.For example, TGH Urgent Care’s case shows that they significantly reduced daily call volumes by 40%, deflecting 40% of calls to SMS messaging by implementing LivePerson’s Voice bot AI. This integration alleviated stress on customer service teams and front desk staff, resulting in a smoother workflow, improved response rates, and a more efficient patient experience while managing an unprecedented surge in patient calls.
Better communication
According to recent studies, 83% of patients reported that poor communication was the most frustrating aspect of their experience. Fear not, as AI technology can also solve this problem. You may wonder how AI enhances communication between hospitals and patients. One of the most effective examples here is patient-centric mobile apps. Patient-centric mobile apps serve as a direct communication channel between clinics and patients, bridging engagement gaps by providing personalized reminders, secure messaging, treatment tracking, and even remote monitoring.
This way, patients receive timely reminders, tailored health advice, and round-the-clock access to assistance, improving their engagement, satisfaction, and adherence to treatment plans. AI-driven solutions streamline healthcare processes, resulting in improved patient outcomes and experiences throughout their healthcare journey.
AI makes diagnoses more accurate
AI holds tremendous potential to enhance efficiency in healthcare diagnoses. By leveraging machine learning algorithms and vast datasets, AI systems can analyze medical images, patient data, and symptoms to aid in identifying and interpreting diseases.
For example, the University of Hawaiʻi Cancer Center employs artificial intelligence to enhance breast cancer risk assessment. Thus, AI serves as a powerful tool for classifying mammograms into low and high-risk categories, aiding in efficient breast cancer screening and diagnosis.Besides, according to findings from the NCBI study, AI-powered algorithms accurately identified 68% of COVID-19-positive cases within a dataset comprising 25 patients initially diagnosed as negative by healthcare professionals.
Gen AI in education and training
Generative AI truly takes medical education and training to the next level by offering dynamic and personalized learning experiences tailored to individual needs. It enhances educational effectiveness through adaptive and interactive content delivery. Let’s see how exactly AI leverages education:
- Real-world role-playing scenarios: Imagine a generative AI-powered platform that creates virtual patient scenarios for medical students to practice diagnosing and treating various conditions, providing realistic learning experiences without the need for physical patient interaction. It’s like a bridge between theory and practice.
- Customized learning paths: What does it mean? The technology guides students through the intricacies of medical education at their preferred speed, pinpointing knowledge gaps, proposing areas for improvement, and delivering customized feedback, thereby enhancing the engagement and efficacy of the learning process.
- Creation of educational content: AI can analyze vast amounts of medical literature and research to generate up-to-date educational materials and textbooks, ensuring that medical students and professionals have access to the latest knowledge and best practices in healthcare.
- Generating case studies: Generative AI can produce case studies resembling real-life scenarios, enabling students to apply theoretical knowledge in practical contexts. These case studies can vary in complexity to accommodate learners’ skill levels. Additionally, it extends to interactive learning modules covering a broad spectrum of topics, ranging from basic anatomy to intricate surgical procedures.
We build AI tools tailored for clinics, hospitals, and healthtech startups.
Limitations and challenges of LLMs and Gen AI in healthcare
Yet, according to research, 60% of Americans still feel uneasy about their healthcare provider depending on AI. So, what is the problem with AI in healthcare, and what are the limitations of generative AI in healthcare?
Data bias
Data bias is a critical concern in developing and deploying LLMs and Gen AI. These advanced systems heavily depend on the data they are trained on, which, unfortunately, can harbor biases reflecting the existing disparities prevalent in healthcare.
What does “data bias” mean? Imagine a facial recognition system used in healthcare settings to identify patients. Suppose the system is trained primarily on data from one demographic group, such as individuals of European descent. In that case, AI may struggle to identify patients from other racial or ethnic backgrounds accurately. This could lead to errors in medical records, misdiagnoses, or delays in treatment for patients whose facial features are not well-represented in the training data.However, bias is just one aspect of the broader ethical AI challenge. Organizations also face serious questions about data privacy, regulatory compliance, and accountability when adopting AI tools. A biased model might misclassify a patient, but a poorly governed one could leak sensitive medical histories or even generate legally inaccurate documentation.
Lack of transparency
We’ve already talked about the lack of transparency as a significant concern of AI in KITRUM’s previous articles, and of course, it is not an exception in healthcare. Without clear insight into how these AI models generate their outputs, clinicians may struggle to interpret and validate their recommendations, again potentially leading to errors in diagnosis or treatment.
For instance, a generative AI algorithm tasked with generating synthetic medical images may produce realistic-looking results. Still, without transparency into the data and processes used, clinicians may lack confidence in the accuracy of these images for diagnostic purposes.
Privacy concerns
As reported by Forbes, 80% of people express worry about using their personal data to train AI models, while 72% harbor concerns about the potential use of their personal data by a forthcoming powerful AI system.
The sensitive nature of patient health data, including medical records and diagnostic images, raises significant ethical and legal questions regarding consent and data protection. For example, if a generative AI model is trained on a dataset of patient images without adequate anonymization or patient consent, it could inadvertently expose individuals to privacy breaches and potential harm.For instance, at Kitrum, we address these concerns directly by ensuring: GDPR/HIPAA compliance at every stage of AI adoption, transparent data flows so patients and providers know how information is used, and role-based access control to restrict sensitive data to only those who genuinely need it. AI often works as a hidden tech partner in software development, which can either accelerate results or introduce strategic risks if not managed carefully; the same is true in healthcare, which requires the same level of oversight. Responsible adoption ensures AI remains a trusted ally rather than a liability.
Future outlook
Looking ahead, the future of generative AI in the healthcare industry appears exceptionally promising. With the market value of AI in healthcare reaching approximately 11 billion U.S. dollars worldwide in 2021, forecasts project an astronomical surge, with expectations that the global healthcare AI market will soar to nearly 188 billion U.S. dollars by 2030.
This remarkable growth trajectory is expected to surge at a compound annual growth rate of 37% from 2022 to 2030. Besides, around a fifth of healthcare organizations have already embraced AI models for their healthcare solutions, highlighting the growing adoption and recognition of AI’s potential to revolutionize patient care and operational efficiency.
Meanwhile, an overwhelming 79% of healthcare professionals foresee robotics and AI as pivotal players in significantly improving the healthcare industry, indicating widespread optimism and anticipation for the transformative impact of AI in shaping the future of healthcare delivery and outcomes.
