Generative AI is revolutionizing the healthcare industry by transforming patient care, diagnostics, treatment planning, and operational efficiency.
This blog explores how generative models such as GPTs and diffusion models are enabling innovations like synthetic data generation, personalized medicine, and automated diagnostics.
This touches on challenges like data privacy, ethical concerns, and regulatory compliance, emphasizing the need for responsible AI implementation in healthcare.
How many times has it happened to you when you've had to write clinical notes and generate summaries, mention the treatment plans, and all that back-and-forth hassle?
This administrative burden is more than just an inconvenience. Spending too many hours on administrative tasks, such as paperwork, overwhelms 64% of doctors and increases their burnout levels, according to a survey from Athenahealth.
Clinicians are overstressed, as they have to keep up with documentation and clinician notes, and they often work an average of 15 hours a week, even at their pajama time (working from home).
The result? They can't focus on the most important person, which is the patient; patient-doctor interactions have become limited.
This is exactly why Generative AI is an asset for doctors and medical professionals, as it makes their life easy in several ways -
Extract billions of medical data points
Suggest a personalized treatment plan
Suggest diagnosis
Let's learn about how Generative AI can transform your healthcare ecosystem.
What is Generative AI in Healthcare?
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.
AI becomes a specialist, helping doctors by scaling their workload, allowing them to spend more time with their patients and provide 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, it is clear how Generative AI is making a positive impact 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 a doctor's clinic.
Generative AI can automatically create doctor's visit notes in the same style and format that physicians use. It includes everything, such as the patient's medical history, complaints, and present illness.
Hence, a doctor does not need to spend more time writing notes because Generative AI does everything based on the conversation during the patient visit.
This was just one of the coolest use cases of Generative AI.
In a nutshell, it can do more than you think and has the potential to transform the healthcare industry. It makes healthcare processes more efficient, helps automate administrative burdens, and allows doctors to spend more time with their patients.
Healthcare AI Market Trends and Global Forecast
The market size of Generative AI in healthcare is expected to rise from USD 3.3 billion in 2025 to USD 39.8 billion by 2035.
It's expected that North America will dominate the global healthcare market by 56% for Generative AI in healthcare category. This region has access to advanced medical facilities, healthcare infrastructure, and the presence of Generative AI healthcare companies.
Nearly 85% of healthcare providers use Generative AI tools to streamline their healthcare operations.
50% of healthcare providers offer virtual assistance-based nursing applications. These healthcare apps free up medical staff by automating administrative tasks.
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.
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.
These healthcare chatbots not only provide round-the-clock support to patients but also guide them throughout the health process and, in some instances, alert healthcare staff.
Minimizes manual inputting of data by extracting and summarizing relevant data from patient records in healthcare databases.
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 was 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 in the range of $1 billion - $2 billion.
The good news? Using generative AI tools in healthcare can reduce such drug discovery processes to 30 months, especially for the pre-clinical phase.
What takes years for traditional drug discovery processes can now be handled by AI. It can quickly identify molecules, chemical compounds, and analyze massive datasets to find new combinations of drug candidates.
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.
Atomwise, a pharmaceutical company uses AI in drug discovery processes and creates better medicines. They predict drug candidates for different diseases through the analysis of potential compounds and complex molecular structures.
The result? AI-enabled drug discovery systems helped them discover 40,000 potential compounds within 6 hours.
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
In the healthcare industry, it's always challenging to provide personalized health care services to patients.
This involves analysis of existing health records, data from wearable devices, and massive datasets from various sources like lab tests, medical reports, patient-reported data, etc., which takes up most of their time.
Generative AI, on the other hand, quickly analyzes large chunks of patient datasets and plays a significant role in personalized medicine. By using advanced datasets and intelligent algorithms, Generative AI can work like an intern to provide patient specific treatment plans.
How does Generative AI help with Personalized medication?
It analyses several datasets such as patient's medical history, generic information and lifestyle factors and then develops tailored care strategies for them. It can identify the patterns in patient data that might be overlooked by traditional methods.
These ML capabilities ensure that medical professionals can detect diseases early on before they become a major issue such as cardiovascular conditions.
By understanding the genomic data and traditional healthcare records, generative AI can create personalized care plans and even suggest optimal dosages and therapies.
Result?
Personalized treatment plans = Better health outcomes and better quality of life for patients
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.
5. Proactively predict risks for catastrophic healthcare events
Healthcare-based generative AI solutions can proactively predict catastrophic healthcare events. Not only can scientists find out how diseases spread, but they can also figure out antibodies for dealing with those infectious diseases.
The EWAD system was trained on the data of SARS-Cov-2 variants, understands the existing data and finds out relationships between them to find out the next variant of pandemic that can infect people.
It can understand the rules of pandemics and predict the life of viruses before they become a major concern for people.
It can even tell them how these viruses can affect people before they start circulating. Such an early warning pandemic tracking system was helpful to medical workers and scientists as they can get a long lead time to deal with those viruses.
6. Synthetic data generation
Synthetic data means artificial generated data that looks real but that's derived either through human experiences or existing data.
If you've the dataset of existing patients who have a rare disease, through generative adversarial networks, you can get new data combinations.
Often, real patient data is always subject to privacy concerns, but Generative AI solutions not just transform the healthcare industry but also keep patient data safe and secure.
Using real patient data always remains a concern for patients and medical researchers, and it's always protected by laws such as HIPPA in the US and GDPR in Europe.
Through synthetic data generation, researchers can test different hypotheses and use the data for research and training purposes.
When to use synthetic data in healthcare?
Limited availability of patient's data
Access to data is restricted
7. Medical training and research
The use of generative AI technology in healthcare can be a game changer in the field of medical training and research.
Through virtual simulations, medical professionals and learners can interact with virtual patients and conduct experiments in a safe and controlled environment.
That's how students learn from virtual simulations, upgrade their knowledge, and get real life exposure by wearing AR/VR headsets.
Apart from creating a safe and controlled environment, Generative AI can adapt to the learner needs and then provide personalized learning experiences.
The AI model knows that a particular student struggles with this medical condition, so it would create a personalized learning path and adapt the entire module and present the cases per the learner's style and preferences.
Through AR/VR simulations, students can perform their medical procedures (dressing wounds, tracheostomy care) etc. Though virtual manikins can't replace real patients, it offers a real-life exposure to students to practice under complex medical situations.
Best part? Learning becomes easy even if patients are not physically available.
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.
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 the few examples of how companies use Artificial Intelligence in healthcare -
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.
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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.
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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.
3. Cleveland Clinic
The Cleveland Clinic has been using Artificial Intelligence to help ICU patients before their health conditions worsen. The AI can identify problems early on, before doctors and medical professionals can notice them.
AI can give medical staff more time to save a patient's life. It extracts data from various sources, including vital signs, lab results, and other healthcare metrics. Early detection of diseases improves patient survival rates.
Artificial Intelligence systems can analyze unstructured medical records, such as discharge summaries and patient histories, to enhance their clinical decision-making process.
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According to Tomislav Mihaljevic, M.D., the CEO and President of the Cleveland Clinic, says
We've been using AI to achieve three key goals: providing better care to patients, refining the environment in which caregivers work, and becoming an efficient medical organization.
They introduced an AI platform "Ambience" for clinicians to -
Transcribing patient appointments
Generating medical notes
Reducing the administrative workload
Improved patient-doctor interactions
”
4. MD Anderson Cancer Center
MD Anderson Cancer Center believes that AI can bring a transformative impact on healthcare.
By integrating AI into their oncology practices, clinicians can provide personalized treatment plans to patients and improve their diagnosis processes.
Traditionally, clinicians used to spend hours identifying tissue samples. With the help of AI and deep learning, they can quickly scan for cancerous cells and provide patients with speedy treatment.
5. Stanford Healthcare
The Stanford healthcare team has been utilizing over 30 AI tools to deliver high-quality care to patients, from reading healthcare images to sending alerts to doctors whenever a patient's condition worsens.
Their medical tool, called Clinical Infomatics Consult Service, serves as a guiding force for clinicians, helping them create more effective care plans for their patients. This AI tool can read a bunch of past patient records.
They've used this tool 1000 times a year.
To reduce the administrative burden on clinicians, Stanford Health Team no longer needs to draft replies to solve patient queries. The AI will generate answers; clinicians can review them before sending an e-mail reply.
Result? Lower stress levels and burnout for clinicians.
What are the Risks (challenges) of using Generative AI in healthcare
Here are a few challenges of combining Generative AI in the healthcare industry -
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.
5. Violates the patient’s privacy
One of the major risks associated with AI in healthcare is that Artificial Intelligence systems are trained on large datasets, which can compromise patient healthcare information.
If an unauthorized party gains access to AI-powered healthcare systems, it can expose sensitive private information.
The only way to protect a patient's confidential information is by implementing robust data security measures. The other option is to comply with data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).
6. AI-generated content can raise ethical concerns
The content that Generative AI systems create appears realistic, but it can also be hallucinated, such as medical images, reports, and diagnostic information. This raises ethical concerns.
The best approach is to train healthcare professionals to review AI-generated output. Artificial intelligence doesn't replace a clinician's expertise; rather, it augments their expertise.
Here are a few steps on how healthcare startups can successfully implement Generative AI -
1. Know your business use case first
Identify what specific problem you want to solve by implementing Generative AI in healthcare.
Do you want to reduce the administrative workload for doctors and medical staff?
Do you want to enhance patient experience through medical chatbots?
Do you want to provide personalized treatment plans to patients?
2. Data collection
Now, you need to collect data from various sources, such as electronic health records, clinical notes, and laboratory imaging. No matter where you collect data, ensure that it is of high quality; otherwise, it will lead to inaccurate outputs and low-quality treatments.
Your medical data should always accurately represent the target population, be secure, and prevent data leaks. If there are any data inaccuracies, then it would lead to adverse outcomes for patients.
3. Select the right AI platform
Not every Generative AI platform can be applied to all areas. Creating a medical chatbot requires a different AI platform that uses natural language processing.
For clinical decision-making, you need a healthcare-focused AI model that can understand medical terms.
Although pre-trained healthcare models are available from various organizations, such as Vertex AI from Google and Microsoft Azure Health Bot.
You can even create custom healthcare solutions for your organization, but if you're not ready to start from scratch, you can fine-tune the model and pre-train it using your data.
4. Hire an experienced app development partner
You need to hire the right app development partner who possesses technical skills, including AI and ML expertise, software development, and knowledge of healthcare systems.
However, if you don't have in-house staff or they lack expertise in AI and software development, consider hiring a healthcare app development company that has a proven track record of creating healthcare software solutions.
5. Evaluate the performance of the AI model
You need to constantly monitor the performance of Generative AI solutions, such as how well they meet user needs. Measure the results that the model delivers, such as
Whether it has improved the time for staff
Whether it has enhanced the patient experience
Generally, AI models become outdated over time, either due to changes in user needs or the availability of new data.
The best approach is to retrain your model, update it with newer datasets, and gather feedback from users who use the AI system. That's how the performance of your AI-based healthcare system will improve.
How BigOhTech can help you implement Generative AI solutions for healthcare startups?
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?
