Generative AI in healthcare: benefits and top use cases

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 use cases across various healthcare areas.

Medical education and training

Practical training using generative AI 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 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 how it aids in medical 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.

Personalized patient care

Generative AI 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 patient data, electronic health records (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 practitioners, this translates to faster and more accurate diagnoses and improved decision-making.

Personalized medicine

Generative AI can help create targeted treatment plans for unique healthcare 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 provide patients with personalized support through conversational interactions. They understand and respond to questions, providing answers based on EHRs, patient data, medical guides and protocols, knowledge bases, and information from previous patient interactions.

Automation of administrative tasks

The healthcare industry involves numerous repetitive administrative tasks that can be automated using generative AI.

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.

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 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 can assist these teams by creating offer letters, customizing onboarding, drafting contracts, creating purchase orders, developing code, running cybersecurity tests, and more.

Benefits of using generative AI

The potential impact of generative AI in healthcare is immense. It 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.

Automating routine tasks

Generative AI can be used to automate repetitive and routine tasks, streamlining processes and making workflows more efficient. This saves time for healthcare personnel, allowing them to focus on tasks that require more attention.

Overcoming data limitations

Traditional medical research and treatment is limited by the amount of information humans can perceive. Generative AI 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.

Enhancing accuracy

Generative AI models are trained on data from various sources and examine various factors that impact decision-making. Thus, these models can identify patterns that may be hidden or initially undetected, improving treatment outcomes.

Reducing human error and burnout

Solutions powered by generative AI 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 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 solutions enable more accurate diagnoses and personalized treatment plans, which increases patient satisfaction and loyalty.

Challenges of implementing generative AI in the healthcare industry

Despite all the advantages of implementing generative AI in healthcare, this technology does come with some risks.

Clinical utility

Before implementing a generative AI model, 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 local policies, such as the GDPR in the EU.

System integration

Implementing generative AI solutions is a key component of the digital transformation process that many healthcare 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 AI tools. It is also essential to cleanse and standardize data so 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. An AI solution must be clinically valid and generate clinically meaningful outputs. However, this can be difficult to evaluate because healthcare companies often rely on their own experience or that of their colleagues.

Where to start when integrating generative AI into healthcare

The best practices for integrating generative AI solutions into healthcare processes 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?

Assess risks

Another important step is to evaluate all the risks and challenges associated with integrating generative AI into 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 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.