6 Ways Generative AI Will Transform Healthcare

Generative AI is ushering in a transformative era in healthcare, with far-reaching implications for patient care, diagnostics, and more. Will the technology help to overcome some of the biggest problems facing our stretched healthcare systems and democratize access to healthcare for all? If these use cases are anything to go by, generative AI certainly has the potential to add very real value to the healthcare sector.

Here are six main ways that generative AI can improve healthcare delivery.

1. Personalized On-Demand Health Advice

Sophisticated generative AI models like GPT-4, combined with human doctor expertise, have led to a new wave of virtual health assistants. Ada is a doctor-developed, AI-driven app designed to assess symptoms and offer patients medical guidance in multiple languages (including English, German, French, Spanish, Portuguese, and Swahili). So far, the app has amassed 13 million users and has completed more than 30 million symptom assessments. It works by asking you questions about your symptoms (you can also create separate symptom profiles for loved ones) and then pointing you to possible conditions and medical guidance. The app also tracks your symptoms as they progress.

With millions of people around the world unable to access medical care (either because of their geographical location, for economic reasons, or simply because their local services are too stretched), we can expect to see generative AI pick up some of the slack.

2. Precision Care From Busy Doctors

I believe we will increasingly see generative AI systems supporting doctors during patient consultations – with the AI operating in the background, listening, taking notes, and formulating potential questions for the doctor to ask based on the patient’s history and symptoms. It would be like a cross between a medical chatbot and a diagnostic tool but designed to be used in one-to-one sessions with patients.

A good example comes from RythmX AI, which has created a precision care platform that helps doctors deliver hyper-personalized care. In essence, the system uses generative AI and predictive AI algorithms to provide patient-specific actions and recommendations. Doctors can then drill into the recommendations via the natural language interface – so it’s rather like an AI co-pilot for doctors. This sort of AI-augmented approach could help doctors get the most out of patient appointments (which can be as brief as 10 minutes).

3. Tailored Treatment And Health Plans

Generative AI may also help doctors enhance patient treatment – by analyzing vast patient datasets to recommend personalized treatment plans, optimizing medication dosages, and predicting potential adverse reactions, all based on the individual. Plus, it can assist in the creation of tailored rehabilitation exercises and therapy programs.

What’s more, Generative AI could help to enhance preventative medicine. For example, clinics and hospitals could employ generative AI to create personalized health plans based on a patient’s unique genetic makeup, health history, and lifestyle.

4. Image Analysis And The Early Detection Of Diseases

AI has been making a mark in diagnostics for a while, but generative AI will significantly enhance the analysis of medical images. As such, we’ll increasingly see generative AI tools being used to help radiologists identify and diagnose diseases from X-rays, MRIs and CT scans with greater accuracy and speed.

One study explored the use of AI to interpret chest radiographs and generate radiograph reports in the emergency department. Since many emergency departments don’t have 24/7 access to dedicated radiology services, images are often interpreted by a remote radiologist (known as “teleradiology”) or even by ER doctors. The study found that the AI tool generated rapid radiograph interpretations and reports with comparable levels of quality and accuracy as radiologist reports – and to a higher quality than teleradiology reports. In one of the cases, the AI performed even better than a human radiologist, detecting an issue that the radiologist failed to report. This shows that not only can AI help radiologists perform their work more quickly and effectively – it can also help clinicians in other departments interpret medical images and accelerate the processing of patients.

5. Accelerating Drug Development

Generative AI is already having an impact on the discovery of new drugs to treat diseases. How? Well, the technology can help researchers understand disease markers more easily and find optimal combinations of chemicals (and even invent entirely new combinations) to create new pharmaceutical treatments. As such, generative AI will accelerate drug discovery and development by generating novel molecular structures, swiftly screening compounds, predicting drug interactions, repurposing existing drugs for new applications, optimizing clinical trials, and enhancing drug formulations.

In the future, this may also help to enhance personalized treatment – because drugs could, in theory, be tailored based on individual patient data.

6. Behind-The-Scenes Improvements In Healthcare Settings

While seemingly not as exciting as discovering new drugs or enabling personalized care, generative AI can help to reduce the administrative burden in healthcare settings – particularly by automating tasks such as medical coding, billing, routine inquiries, and notetaking. It makes a lot of sense when you consider the tasks that generative AI is capable of writing, listening, interpreting human speech, and understanding the text.

One good example comes from NextGen Healthcare and its Ambient Assist notetaking tool, which listens to conversations between patients and clinicians and then provides summary notes. Notes are available for the clinician to review within just 30 seconds of completing the patient encounter, with the tool documenting appointments with over 90 percent accuracy. Therefore, tools like NextGen help clinicians cut down on admin tasks without compromising clinical records. Given that admin burden is cited as a leading cause of clinician burnout, anything that can lighten clinicians’ admin burden could add enormous value to our healthcare systems.

Clearly, there’s no substitute for receiving amazing care from human doctors and other healthcare professionals. But it’s clear that generative AI offers solutions that can help to bridge the gap between growing healthcare needs and apparently dwindling healthcare resources. As healthcare systems become more stretched, combining human and machine expertise will likely be the best way to diagnose patients and provide appropriate treatment.