Generative AI in Healthcare – Use Cases, Benefits, and Limitations 

With so much unstructured data produced each day, Generative AI in healthcare proves to be a game-changer for healthcare organizations. 

It automates time-consuming administrative tasks by transforming patient interactions into clinical notes in seconds. 

This results in increased patient satisfaction, and healthcare professionals can build closer relationships with customers. 

Earlier, doctors took time to read and analyze logs and data, but now Generative AI is there to synthesize medical information and help clinicians by generating medical drafts and summaries. 

According to a report published by Deloitte, AI in healthcare is just the beginning, and it offers exciting opportunities going forward for consumers and healthcare stakeholders. 

Currently, the healthcare ecosystem is facing various issues such as: 

• There is a shortage of healthcare professionals. 
• Clinicians are experiencing burnout due to heavy workloads. 

• Patients are not receiving personalized attention and treatment plans, affecting the quality of health outcomes. 

To tackle all this, healthcare organizations should leverage this groundbreaking technology, aka Generative AI. 

By the end of this blog, you will learn about Generative AI in healthcare, the potential benefits it provides, and its applications. 

What is Generative AI? 

Generative AI involves training AI models so that they can understand patterns and structures within existing content. 

It then uses the same patterns to learn from existing data and come up with new output such as text, audio, video content, images, etc.  

A few instances of Generative AI include ChatGPT, Dall-E, Bard, Mid-journey, etc.  

Generative AI works on two different models – Large language models (LLMs) such as ChatGPT/Bard and Image-based models. 

Generative AI has several use cases such as acting as a conversational chatbot, generating text, writing code in different languages, and analyzing e-mails and data. 

What is Generative AI in Healthcare?  

Large language models are becoming more exciting because they provide you with an interface where you can talk to them. 

In healthcare, Generative AI is not an exception. It can impact the drug design process and create new drugs faster. It changes the way you think earlier.  

Here, AI becomes a specialist and helps doctors or scales the doctor and lets him spend more time with his patients by providing quality care.  

As per Accenture research, AI enhances the work of 40% of healthcare providers’ working hours. 

More than half of healthcare organizations plan to leverage ChatGPT for learning purposes. 

From these figures, you can understand how Generative AI is showing a positive mark in the healthcare sector by transforming healthcare processes and facilitating clinical workflows. 

Dr. Bricker (Healthcare Finance) explained that Generative AI can be combined with other technologies such as Ambient voice recognition and Natural language processing.  

Often, doctors and healthcare professionals used to spend more time writing notes in a hospital or at the doctor’s clinic. 

Generative AI can automatically create doctor’s visit notes in the same style and format that physicians create.  

It includes everything such as the medical history of a patient, patient complaints, present illness, etc.  

Hence, a doctor need not spend more time writing notes because Generative AI does everything based on patient visit conversation.  

Currently, DeepScribe (a medical transcription platform) creates AI-generated clinical notes based on doctor-patient conversations.  

The technology saves an average of 3 hrs./day for a doctor by reducing their burden from tedious data entry. This was just one of the coolest use cases of Generative AI.  

In a nutshell, it can do more than what you think and has the potential to transform the healthcare industry. 

It makes healthcare processes more efficient, helps you automate administrative burdens, and lets doctors spend more time with their patients. 

What are the use cases of Generative AI in Healthcare?  

Generative AI has a wide range of applications in the healthcare industry, such as: 

1. Automate administrative burden 

The most popular use case of Generative AI in healthcare is that it automates administrative tasks.  

Studies show that 62% of the US physician workforce felt very stressed in 2021.  

It leads to further consequences for patients when those overwhelmed doctors treat patients because they think of hurting themselves all the time. They had huge loads of administrative burden on them.  

With Generative AI, managing administrative processes now becomes easier as it takes the burden off doctors’ shoulders and saves their costs. 

Data reports state that the US spends 15-30% of the healthcare money that goes towards administrative processes. 

Moreover, Healthcare institutions spend 2x the spending required for cardiovascular disease and 3x the spending for cancer care. 

But Generative AI solves the problem of healthcare administration in the following ways:  

• It automates the booking processes and schedules/reschedules patient appointments based on the needs of patients and the availability of doctors. 
• Reduces the amount of paperwork by automating routine documentation tasks such as automatically creating medical notes, updating patient records, etc.  
• Makes the claim process faster, leaving no room for manual errors.  
• Healthcare staff can now focus on high-value tasks because AI-powered chatbots are there to handle routine queries.  
• Minimizes manual inputting of data by extracting and summarizing relevant data from patient records in healthcare databases. 

For Instance – Microsoft is collaborating with Epic (healthcare vendor) by integrating generative AI into Epic’s EHR software. This way, healthcare professionals can boost their productivity and enhance their patient care. 

Navina, a medical AI startup, came up with a generative AI assistant that saves hours for doctors as they can get insights about patients’ healthcare status through natural language interactions. 

They don’t need to juggle anymore with time-consuming administrative tasks. 

The AI-powered assistant automatically generates progress notes and integrates everything into electronic health records with a single click. 

2. Helps in drug discovery and development  

The traditional drug discovery and development process were time-consuming as it took around 12-18 years to create the drug. Only 10% of these drugs become successful in clinical trials.  

Additionally, the overall cost of developing a new drug comes between the range of $1 billion – $2 billion. As compared to traditional drug discovery methods, pharmaceutical companies that use generative AI speed up drug creation and testing processes by 40-50%.  

Besides that, it saves their costs around $26 billion annually, which was earlier spent during the research stage.  

A recent example of how Generative AI revolutionizes healthcare is Google.  

It partnered with various pharmaceutical companies such as HCA Healthcare, Meditech, etc., to speed up the drug discovery process. 

One such pharmaceutical company leveraged Google’s generative AI solutions (Google Cloud Vertex’s AI and MedPaLM 2) to make the complex drug discovery process faster.  

Otherwise, creating new drugs with the traditional drug discovery process takes around 12-15 years, which is a time-intensive task. 

Here’s how Generative AI in healthcare makes the drug discovery process faster:  

• It studies existing biological datasets to create drug-like molecules. Then, scientists perform testing of new molecules in the lab.  
• Generative AI helps in developing customized treatment plans for each patient by analyzing patient-specific data, including genomics and proteomics.  
• It enhances the efficiency of the drug development process by studying past data from clinical trials and identifying the right patient groups.  
• Generative AI saves substantial costs for pharmaceutical companies by reducing the need for expensive physical testing of drugs. 

3. Medical Imaging  

Medical imaging allows radiologists to allow the doctors to diagnose injuries and diseases. Here, doctors need not rely on making random guesses as they can use various medical imaging procedures (X-rays, CT scans) to provide effective treatment to patients. 

However, understanding medical images requires extensive time and effort from radiologists. 

By applying Generative AI to medical imaging, healthcare professionals can accurately interpret images quickly and can tailor their treatment plans accordingly. 

There are 2 types of Generative AI imaging models – Generative Adversarial networks (GANs) and Variational autoencoders (VANs).  

It improves the accuracy of diagnosis by generating synthetic images to provide better patient outcomes. 

Generative AI in medical imaging analyzes data points from a bird’s-eye perspective and can distinguish between disease and healthy segments. 

4. Personalized medication 

Earlier, it was challenging for healthcare professionals to provide personalized care to each patient. 

Analyzing massive datasets from various sources like lab tests, medical reports, patient-reported data, etc., takes most of their time. 

Generative AI, on the other hand, quickly analyzes large chunks of patient datasets. 

Now, the data gathered has to be communicated to doctors and patients. Then, AI models create patient-friendly explanations for healthcare professionals so that they can simplify complex genetic information in a simplified way.  

Later, they can come up with customized treatment plans based on the patient’s genetic makeup and medical history. 

Researchers in Pennsylvania, for instance, developed a Generative AI model that predicts the response of individual patients and then comes up with personalized treatment plans based on the individual characteristics of each patient. 

Benefits of using Generative AI in Healthcare 

1. Managing medical records becomes easier 

According to the World economic forum, the healthcare industry faces a data nightmare an average hospital produces around 50 petabytes of data annually. 

Out of that, 80% of the data is unstructured, which is hard to deal with. 

Because the data is voluminous, there is a need for AI technology to gather and record medical information in one place.  

This is where Generative AI comes into play. It uses deep learning algorithms to create new content be it text, code, audio, images, or any other content.  

Such AI tools are going to save tons of time and money for your healthcare business as they analyze unstructured medical information to come up with data-driven insights in the future. 

2. Provide personalized medication to patients 

According to a health insights analyst at IDC, healthcare organizations see massive potential in Generative AI. 

64.8% of healthcare organizations are exploring Generative AI, and 35% have already invested in this technology.  

Generative AI analyzes a lot of information such as- 

• Genetic health records,  
• Predicts how a person responds to certain treatments, 
• Reviews a patient’s medical history, 
• Lifestyle choices, and  

• Considers other factors to offer personalized treatment plans so that each patient receives the best possible care. 

3. Improves patient engagement 

Generative AI in healthcare is a boon in the healthcare industry as it actively engages with patients.  

These AI-powered chatbots monitor patients’ health-related metrics and provide on-demand information to them, send health reminders regularly, and answer their queries.  

Virtual Medical assistants provide 24*7 support to patients and effectively handle patient questions. 

Also, when treatments are customized as per their preferences and lifestyle choices, their overall satisfaction increases. 

Such AI-powered tools bridge the communication gap between healthcare providers and patients. 

UNC Health, for instance, used Epic, a Microsoft generative AI tool for drafting responses to time-intensive patient messages. 

This increases patient engagement as the front-line medical staff can now spend more time with their patients and less time on their computers.  

4. Reduction in cost 

By automating administrative tasks, Generative AI in healthcare reduces overall healthcare costs. 

It helps healthcare providers identify patients with high risk so they can prevent unnecessary hospital visits and provide better treatment to them. 

They need not worry about investing in advanced medical equipment. 

On the other hand, it promotes preventive care before medical conditions become worse. 

For Instance – By automating image analysis, healthcare professionals can better serve their patients because analyzing patient data becomes easier than ever. 

5. Safer surgeries 

With Generative AI in healthcare, doctors can plan out their surgeries in a better way.  

After analyzing medical images and patient information, AI models can spot consequences and help doctors prepare themselves for future surgeries. 

At the same time, it provides real-time assistance to doctors so they can carry out the surgery smoothly. Thus, it leads to more successful surgeries with fewer problems. 

Examples of Healthcare Companies that are using Generative AI in Healthcare  

Here are a few healthcare companies that are making the best use of Generative AI – 

1. HCA Healthcare 

 Healthcare Corporation of America (HCA), a leading healthcare provider, has collaborated with Google Cloud to ease their administrative tasks so they can focus on providing better care to patients. 

The managing director of HCA Healthcare said – Generative AI provides better support to nurses and doctors and helps them achieve one goal, which is caring for patients. 

Earlier, doctors used to spend more time on writing clinical notes, now Generative AI will automatically create drafts by listening to the doctor-patient conversation.  

This technology saves doctors hours as they don’t have to do manual entry or dictation.  

When this AI-powered technology creates medical notes, the doctor then reviews the draft before transferring it to an electronic health record system. 

2. MEDITECH  

It was challenging for clinicians at MEDITECH to analyze loads of medical information that is stored across multiple systems.  

With the Med-PaLM 2 solution, clinicians can get a quick snapshot of a patient’s medical history. 

This technology provides immense potential for clinicians as they just have to ask a series of questions from the model, and it will provide access to patient records, clinical guidelines, and other research articles.  

Additionally, it automates the process of creating clinical documentation and generating summaries on the go. It saves time for doctors and lets them give utmost attention to serving their patients. 

Limitations of Generative AI in Healthcare 

Here are the challenges of using Generative AI in healthcare – 

1. Biased data  

As Generative AI is trained on biased data, it is projected that it may produce biased results for all populations.  

This becomes a serious concern in healthcare as data on which Generative AI is trained results in unequal treatment for different population groups.  

The new data produced by Generative AI results in biases based on age, sex, socioeconomic status, and gender.  

For example – A research study was conducted that states that Generative AI produced biased results in diagnosing people with darker skin tones. Such results lead to disparities in healthcare based on race or gender. 

2. Lack of regulatory standards 

As Generative AI is trained on huge datasets, there comes the concern for data breaches if there is mishandling of patient data.  

In the healthcare industry, healthcare organizations must abide by regulatory norms such as HIPAA and GDPR. 

 HIPAA was introduced to safeguard the health information of patients.  

Although Generative AI in the healthcare industry provides several advantages, as these tools are not approved by any healthcare system, their utility is still limited.  

These AI-powered tools are still using patient information without obtaining their consent to produce more insights. 

3. Provides inaccurate information 

Generative AI saves hours for doctors and lets them pay attention to their patients.  

However, they are not 100% accurate, as these models are fine-tuned to provide specific kinds of responses. 

 These models are proficient in predicting what would be the next word in a sentence. However, inaccurate prompts lead to inaccurate responses. 

And in the healthcare sector, such misinformation cannot be tolerated, as it could affect the quality of patient care and human lives. 

Hence, you can’t expect LLMs to help doctors make more informed decisions.  

However, doctors can rely on these models for generating summaries of patient appointments or writing clinical drafts. But they still need a person to review these drafts. 

4. Overreliance on AI is not acceptable 

The best way to use AI in healthcare is to include a human in the loop. Through this approach, you are training AI to think like a human. 

Here, you are training AI and fact-checking results so that the model learns to provide output in the same way as expected. 

Moreover, responses generated by these models can lead to harmful medical decisions if a medical professional is not involved. 

Also Read: Benefits Of Machine Learning In Healthcare

Wrapping up  

Generative AI redefines the future of the healthcare landscape in various ways such as  

• It provides customized treatment plans for each patient  
• Streamlines drug discovery and development process  
• Improving diagnosis  
• Providing better care to patients  
• Reduces administrative burden  

and countless benefits coming forward.  

By leveraging such AI-powered technology in healthcare, doctors and healthcare staff can enhance their patient experience and help them make more informed decisions. 

However, you need to integrate such AI solutions in healthcare by complying with regulatory requirements so that the privacy of patients can’t be compromised.  

At BigOhTech, we are here to help you achieve your business goals by building data-driven models for you. 

Having 5+ years of experience in AI/ML consulting, our dedicated AI/ML experts are here to develop conversational medical chatbots for your healthcare business that improve your patient engagement rate. 

Need help in developing generative AI solutions for your healthcare business?