Junior doctors often care for hundreds of patients during a single shift.
A recent survey found that one in three UK medical students left the NHS within two years of graduating—either to practice abroad or change careers completely.
This shows how much stress new doctors are under, leading to a loss of valuable talent and impacting the quality of patient care.
But with great challenges come great opportunities.
For aspiring entrepreneurs and healthcare startups, generative AI offers a chance to develop products that give doctors more time, reduce burnout, and improve clinical decision making.
In this article, we’ll explore how generative AI is transforming healthcare and the latest trends you can leverage in your own startup.
What is Generative AI in Healthcare?
Generative AI is a type of artificial intelligence that can create new content, such as text, images, or sounds. It does this based on patterns it learns from existing data.
For example, if a doctor needs to write a summary of a patient’s medical history, generative AI can analyse past patient records and automatically generate a detailed report.
There are many applications of generative AI, of which the following are the most widely used:
🤖 Large language models (LLMs): These AIs use natural language processing to read and write text like a human. In healthcare, they help by drafting patient notes, answering questions, and supporting medical decisions.
🤖 Generative adversarial networks (GANs): While not strictly an AI, this machine learning architecture allows two AI systems—a generator and a discriminator—to compete. The generator creates data while the discriminator evaluates it, leading to ongoing improvement. In healthcare, GANs can enhance medical images, generate synthetic data for training, and aid in research.
🤖 Text-to-Speech (TTS) Models: This can convert written text into spoken words. It’s useful for creating accessible content for patients with visual impairments or literacy challenges.
By using generative AI in healthcare, doctors can speed up their administrative tasks so that they can spend more time on direct patient care.
This might also help doctors feel less overwhelmed, increasing the quality of care they’re able to provide and preventing them from abandoning the medical field altogether.
Where AI is Making a Difference For Healthcare Professionals: 7 Use Cases
Since the early 2010s, generative AI has made a big difference in the healthcare industry.
By improving the day-to-day lives of healthcare providers, it has ensured that patients receive better care in medical facilities.
Here are some of the core ways in which generative AI is being used in the healthcare industry:
1. Review records quickly during emergencies
While patients are in the emergency room, doctors have to perform what they call a “chart biopsy,” which is the process of extracting relevant information from a patient’s medical records.
This involves sifting through extensive notes and data to identify key details for accurate medical diagnosis and treatment planning.
Sometimes this can take hours, wasting valuable time that could be spent on direct patient care and healthcare delivery.
This is particularly difficult in the ER, where doctors are pressed for time and need to get their patient’s history before performing an emergency operation.
Using generative AI can save lives by giving healthcare professionals access to all the necessary information before making life-or-death decisions.
For example, Dodon AI allows doctors to get an overview of their patients’ medical history to make more informed decisions and quickly identify potential risks.
Have a look at this video that explains how it works:
Instead of being drained by reading lengthy medical records, doctors can use generative AI so that they can focus on what matters.
2. Use AI as a doctor’s scribe
Doctors spend 62% of their time completing electronic health records (EHRs), according to a Stanford Medicine report.
Nearly half of primary-care doctors feel that EHRs make it harder for them to provide good care.
A major issue is the time doctors spend writing up notes after each consultation, which can take up to 20 minutes per patient.
Here’s where AI can make a difference: Imagine if your doctor had an AI tool to handle note-taking.
This tool could listen to the consultation, automatically create detailed notes, and provide your doctor with the necessary information to quickly update the EHR at their clinical practice.
This would streamline the administrative process, allowing doctors to spend less time on paperwork and more time with patients.
Here are three examples of how businesses are using healthcare artificial intelligence to help with this:
- DeepScribe: Records conversations between doctors and patients and creates detailed notes that doctors can review and edit.
- Nuance Dragon Ambient eXperience: Automatically listens to patient interactions and creates notes that can be adjusted by doctors later.
- Suki AI: Captures and summarizes spoken notes during patient visits, making the documentation process much easier.
Using these AI tools can save doctors up to 3 hours a day and cost about one-sixth of hiring a human scribe, helping to make their work more efficient and less stressful.
3. Improve medical imaging and diagnostics
In 1971, the first CT scan helped healthcare organisations detect a brain tumour that was previously challenging to identify with conventional imaging methods.
Once the scan was ready, the doctors gathered around and carefully examined the cross-sectional images.
This has been an effective way to visualise internal structures in detail, allowing for more precise diagnosis and treatment planning.
However, there have been cases where doctors have encountered challenges, such as differentiating between similar-looking conditions.
Generative AI is now addressing these issues by enhancing image quality, automating analysis, and providing additional insights.
This has made it possible for doctors to diagnose conditions more accurately and reduce the likelihood of missed or incorrect diagnoses.
Kheiron Medical Technologies is currently using artificial intelligence to assist radiologists by analyzing mammograms:
Another example is PathAI, which uses AI to assist pathologists in analyzing tissue samples.
It’s used in post-mortem examinations, often as part of the autopsy process, to assist pathologists in determining the cause of death and understanding disease progression.
Get a better understanding of what this entails by watching this video:
4. Use chatbots for patient interaction
AI chatbots are automated tools that talk to users through text or voice. They’re used for the following tasks:
💬 Answering questions: A chatbot on a hospital website might quickly answer common questions like “What are the visiting hours?” or “How do I prepare for surgery?”
💬 Guiding users: A chatbot can walk patients through the steps to book a medical appointment, such as choosing a date, and time, and providing necessary details.
💬 Collecting information: Before a visit, a chatbot might ask patients about their symptoms or medical history to help doctors prepare for the consultation.
💬 Handling tasks: A chatbot can handle routine tasks like scheduling follow-up appointments or sending reminders for medication refills.
In 2022, the Lancashire and South Cumbria NHS piloted an AI chatbot that helped patients move up waiting lists by evaluating their conditions.
As a result, this initiative removed 15% of people from the waiting list, improving access for those who needed care more urgently.
In another case, a chatbot was created to quickly answer patient questions at King’s College Hospital NHS Trust.
Within the first weeks of being active, it quickly saved staff around 1,095 minutes that they would have usually spent on the phone answering questions.
A great example of a healthcare business using a chatbot is Ada Health, which asks users about their symptoms and provides potential diagnoses and next steps:
Most recently, they even added Swahili and Romanian to the languages their chatbot can assist users in, making health care accessible to more people globally.
Ada Health’s chatbot offers a convenient way for people to get initial health information and decide whether they need to see a doctor.
5. Predict health risks in patients
Generative AI builds on traditional AI methods, which have long analysed past healthcare data to predict future health risks.
In healthcare, traditional AI can analyse health information to foresee potential problems, allowing early intervention.
However, generative AI takes this a step further by not only predicting these risks but also generating personalised treatment plans based on the recommendations.
For example, if your grandmother had Type 2 diabetes and you’ve been showing minor symptoms like:
🩺 Occasional high blood sugar levels: Sometimes, your blood sugar levels are higher than normal, even if it only happens now and then.
🩺 Fluctuating weight over the years: Your weight has been changing up and down over a long period, instead of staying steady.
These symptoms might seem minor and could be overlooked by your general practitioner (GP).
However, when combined with your family history of diabetes, an AI algorithm can detect patterns that suggest a higher risk of developing Type 2 diabetes.
Generative AI can also create a personalised treatment plan, recommending further tests or lifestyle changes to manage the risk early and improve health outcomes.
One example of generative AI in action is Health Navigator.
One of their users, Peter Elcock, who struggled with diabetes and other chronic conditions, benefited from AI detecting his high-risk profile:
This early detection, coupled with a generative AI-driven treatment plan, led to improved health management, better well-being, and effective support for his condition.
6. Speed up drug discovery and development
Drug discovery and development is the process of finding and creating new medicines.
It starts with testing various ideas, like trying out different plants and natural substances, or combining different molecules to find promising, new drugs.
Once a potential drug is identified, it goes through multiple testing stages to ensure it’s safe and effective before it can be approved and used by patients.
Generative AI systems can help predict which changes might make a drug work better or have fewer side effects, speeding up the development of more effective medicines.
It can further assist by:
🔬 Creating patient profiles: This helps in designing clinical trials and understanding which patients might benefit from certain treatments.
🔬 Simulating trial outcomes: This allows researchers to see which patient profiles might work best, helping to plan more effective recruitment strategies.
For example, BenevolentAI uses generative AI to find new drug candidates and speed up the drug development process.
These are the projects that are currently in their pipeline, including drugs for conditions such as ulcerative colitis and Parkinsons disease:
Their AI system analyse large amounts of biomedical data to suggest new drug compounds and predict how well they might work.
7. Create ChatGPT for doctors
Another time-consuming task that your doctor has to take care of is drafting letters of medical necessity.
For example, let’s say that after a consultation they determine that you need a specialised drug for rheumatoid arthritis.
They would then have to write a detailed letter explaining why this drug is essential, referencing scientific evidence to support their request.
This letter would then be sent to your insurance company or a healthcare authority to get approval for the drug.
While ChatGPT can help with general writing, it’s not suitable for sensitive medical tasks because it doesn’t meet HIPAA standards that require protecting patient data.
On the other hand, businesses like Doximity GPT are designed for health care, which means that it has a zero-data retention policy and they will hold up against regulatory bodies:
It also integrates with your doctor’s Doximity profile, making tasks like creating letterheads and signatures easier.
Opportunities For New Businesses in AI Healthcare
When generative AI, like ChatGPT, became widely available, new businesses quickly emerged to take advantage of this breakthrough.
The health care industry is no exception, offering numerous chances to enhance doctors’ lives and address challenges like burnout.
For entrepreneurs keeping an eye on these trends, there are exciting opportunities to explore. Here are some of the most notable trends:
✅ Funding is readily available
In August 2024, Feryal Clark, the UK’s Minister for Digital Communication and AI, announced that £32 million will be invested in various AI projects.
Among these initiatives are systems designed to streamline prescription delivery, making it easier for patients to receive their medications.
This means that there’s a lot of funding available for businesses that are ready and willing to incorporate AI into their healthcare solutions.
If you have innovative AI technology in the UK, the NHS Innovation Accelerator can offer funding, expert mentorship, valuable resources, and connections with NHS leaders.
✅ Expansion of electronic health records
By November 2023, 90% of NHS trusts had adopted electronic patient records, and this number continues to grow.
Generative AI thrives on large amounts of data, so the more data available, the better it can perform. This expansion creates two key opportunities for your healthcare business:
💥 More business opportunities: As more data becomes available, you’ll be able to identify new ideas that can benefit doctors and patients.
💥 Better functioning businesses: With richer data, AI can become more accurate, improving user experience and enabling the development of healthcare tools.
When data on patient medication adherence was made available, it allowed the UK-based company Lloyds Direct to expand its prescription management service:
This service sends reminders to patients to take their medications and automates the repeat prescription process.
✅ Integration with wearable technology
The number of smartwatches in use in the UK is expected to reach 12.88 million by 2029, a 73% increase from 7.46 million in 2024.
These watches gather an extraordinary amount of health data, such as heart rate, sleep patterns, physical activity levels, and even blood oxygen saturation.
If you’re interested in launching an AI-driven business in the healthcare sector, you need to keep an eye on how this data can be used in your business.
For example, you could develop AI tools that analyse this data to provide personalised health recommendations, track early warning signs of health issues, or even help doctors monitor patients remotely.
There are hundreds of ways you can use AI systems to leverage this data and create a profitable business that also helps improve the lives of patients and doctors.
How MOHARA Can Support Your AI Journey
Generative AI is improving healthcare by tackling pressing issues like doctor burnout and improving patient care.
From speeding up medical record reviews to improving drug discovery, AI offers exciting possibilities for new businesses.
With the rise of digital health records and wearable tech, there’s a wealth of data that can drive innovation and create valuable solutions for doctors and patients.
If you’re an entrepreneur looking to dive into the healthcare AI market, consider partnering with a company that can guide you through integrating AI into your business.
MOHARA specialises in helping startups harness AI to make a real impact. Get in touch with us to turn your ideas into successful healthcare solutions.
