Unless you’ve been hiding under a rock, you’ve doubtless heard about ChatGPT and the amazing recent advances coming out of the field of generative AI. These large language models can now write blog posts, marketing copy, functioning code, poems, exam questions, and many other things, in dozens of languages, almost instantaneously.
Naturally, there’s a rush to apply this technology in all sorts of domains, and our focus today will be on the top use cases for generative AI in healthcare.
What are other businesses doing and how can you employ this cutting-edge technology to add value through your solutions? Whether your business provides a patient-facing app, or an app for clinicians, here are some ways to integrate generative AI into your services.
Let’s get going!
The sections that follow cover how generative AI can be applied to healthcare to benefit patients, and how it can benefit healthcare workers.
Chatbots are algorithms that are capable of processing and responding to natural language. If you’ve used a customer service portal at your bank or an online store in the past couple of years, the chances are pretty good that you’ve interacted with a chatbot.
Chatbots are an excellent way of applying generative AI to healthcare, and there are myriad ways in which they can help improve the patient experience. They have long been used to answer simple questions or to help people find information, and with the major step forward language models have taken, they’re better at this than ever before. Chatbots can now be carefully fine-tuned on a specific set of content, like technical documentation or information about medical procedures, meaning that they are capable of answering much more detailed and subtle questions.
What’s more, when paired with something like a virtual assistant, chatbots can help automate substantial amounts of the bureaucratic side of healthcare administration. This means far less time spent on mundane tasks like setting or rescheduling appointments, transcribing notes, or answering billing questions.
A related (but distinct) point is that generative AI are both now capable of personalizing responses to better suit the needs of a specific individual. For example, a chatbot using sentiment analysis could modulate the tone of its replies based on a patient’s emotional state. Sentiment analysis is an area of machine learning that focuses on training algorithms that can detect the sentiment of text, i.e. whether its overall emotional tone is positive, negative, or neutral.
This could improve the patient experience by helping prioritize and sort patients based on their mental state. A chatbot equipped with this information would be able to focus on the most distressed patients first, phrase replies that are much more empathic and comforting, or more quickly route such patients to a human that can help them.
Artificial intelligence can also help with translating between natural languages, such as English and Spanish or Hebrew and Mandarin. Generating high-quality automated translations was one of the first triumphs of machine learning, and the techniques have only gotten better in the years since. If a doctor finds themselves needing to communicate in another language with a patient, they can now lean on any of a variety of different machine language translation services. This means a reduced risk of medical errors and problems later on, and a better customer experience overall.
We’ve all waited in the waiting room or emergency room for what feels like too long. But AI is changing that. Thanks to AI, we’re seeing billboards pop up across the country that say things like, “ER Wait Time 10 Minutes”. That’s because AI agents can be used to analyze admissions, discharge and transfer (ADT) data and predict what demand will look like on any given day and time. Then, AI can be used to make recommendations that can help staff reduce ER wait times, like sending less severe patients to the nurse instead of the doctor, or managing staff scheduling appropriately.
Patients don’t always take their medications correctly, and doctors don’t always get dosing recommendations right. In fact, 7,000-9,000 people die annually in the U.S. from taking their medications incorrectly, but AI could help fix this.
A Nature Medicine study used an AI monitoring system that operated in patients’ homes to see how they were using either an inhaler or an insulin injector. It was able to detect when they hadn’t followed the appropriate steps. By adding generative AI to this tool, businesses could send alerts to those patients to correct their procedure.
AI is being used to diagnose ailments sooner, like recognizing breast cancer in early stages in CT scans, which significantly increases the chance of survival. And there are AI solutions that can identify patients who are at high risk of diseases like Alzheimer’s, which can help drive preventative care that slows the progression of the disease.
Surgeries are some of the most difficult and complex procedures a human being can perform, and even the best surgeons sometimes slip up and make mistakes. Robotic surgery machines are allowing for far more precision in these operations, reducing the likelihood of catastrophic errors. And robotic surgeons can perform many kinds of procedures through tiny incisions, reducing the damage done to the body and the time required to heal.
Additionally, robotic surgery can be performed from anywhere in the country. Patients wouldn’t need to travel to the facility of a particular specialist, improving convenience for the patient.
AI-powered virtual assistants have the potential to automate substantial amounts of the drudgery facing healthcare staff. There are Virtual Assistants that can schedule meetings, transcribe and annotate notes, follow up with patients, and handle administrative tasks so that clinicians can focus on healthcare.
Services like Microsoft DAX allow clinicians to draft clinical notes automatically, reducing hours of work to mere seconds and enabling them to spend more time with patients and less time typing up notes.
All hospital facilities monitor patients’ vital functions, but AI could help us use this information to predict deteriorations in patient health before they happen. That way, healthcare providers can intervene proactively. This is particularly useful in intensive care units and for monitoring chronic conditions.
Using Large Language Models, you can analyze large amounts of healthcare information and identify trends. Then, you could train an AI to make recommendations or call out insights that could potentially lead to advancements in medicine or patient care. It can generate molecular structures and predict their potential effectiveness.
Drug Discovery
Generative AI can analyze vast datasets from genomics, proteomics, and metabolomics to identify potential drug targets. AI algorithms, particularly those based on deep learning, can generate new chemical entities that are predicted to have high affinity for a target, optimal pharmacokinetic properties, and minimal toxicity. This can significantly reduce the time and cost associated with traditional hit-to-lead and lead optimization phases.
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Generative AI has exploded into the public imagination, and people are finding myriad ways of applying it. Healthcare is a domain that can benefit enormously from AI’s innovative potential, delivering a higher standard of care for less expensive, helping clinicians increase efficacy, and delivering value across the entire sector.
Are you ready to integrate generative AI into your healthcare solutions? If so, you might not be exactly sure where to start. Download the free Guide Getting Started with Generative AI in Healthcare and start planning your adoption roadmap today.
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