How Generative AI & ChatGPT Impact Healthcare Industry

Utilizing deep learning algorithms and neural networks, Generative AI, a sophisticated class of Large Language Models, has the remarkable capacity to generate fresh, original content.

Originating from relatively rudimentary applications in the 1950s, it has undergone a significant evolution. Owing to the exponential growth in data availability and computational power, its applications have dramatically diversified and complexified.

Today, it is revolutionizing diverse sectors, notably the healthcare industry. Here, it is deployed for a wide range of tasks, from intricate data analysis to nuanced natural language processing tasks, driving unprecedented advancements that transform healthcare itself.

ChatGPT is a cutting-edge application of Generative AI, leveraging advanced deep learning techniques to create conversational experiences.

As a state-of-the-art language model, ChatGPT can generate human-like responses based on input prompts, enabling interactive and engaging interactions with users. Its capabilities exemplify the power and potential of Generative AI in transforming communication and user experiences.

Utilizing deep learning algorithms and neural networks, Generative AI, a sophisticated class of Large Language Models, has the remarkable capacity to generate fresh, original content.

Originating from relatively rudimentary applications in the 1950s, it has undergone a significant evolution. Owing to the exponential growth in data availability and computational power, its applications have dramatically diversified and complexified.

Today, it is revolutionizing diverse sectors, notably the healthcare industry. Here, it is deployed for a wide range of tasks, from intricate data analysis to nuanced natural language processing tasks, driving unprecedented advancements that transform healthcare itself.

ChatGPT is a cutting-edge application of Generative AI, leveraging advanced deep learning techniques to create conversational experiences. As a state-of-the-art language model, ChatGPT can generate human-like responses based on input prompts, enabling interactive and engaging interactions with users.

Its capabilities exemplify the power and potential of Generative AI in transforming communication and user experiences.

There are industries where the application of generative AI, including ChatGPT, is clear and straightforward. For Instance, Using ChatGPT in the insurance industry or utilizing Generative AI in HR. Then there are other industries, like healthcare, where it requires a bit more digging to understand the true potential of generative AI technology.

But once you lean in and study what’s happening behind the scenes, one thing becomes clear: Generative AI, including ChatGPT, and the development of these technologies have massive implications for the world of medicine.

With all of that being said, let’s dig in and look at some of the potential benefits:

One of the more interesting corners of the industry where generative AI can impact healthcare is in the screening and diagnosis of chronic diseases. Having the ability to analyze vast amounts of data, including medical records, imaging studies, and genetic data, allows generative AI to identify key patterns and relationships that may not be apparent to human health experts.

For example, researchers at Stanford University developed an AI algorithm that can diagnose skin cancer with the same accuracy as dermatologists. The algorithm was trained on more than 130,000 images of skin lesions, allowing it to recognize patterns and distinguish between benign and malignant lesions.

In addition to skin cancer, AI algorithms have been developed to detect lung cancer, breast cancer, and other types of cancer with high accuracy. If you think about it, it’s pretty amazing what these tools can accomplish.

Generative AI can also be used in predictive maintenance, which improves the performance and reliability of medical imaging equipment. (Not to mention, it makes managing the equipment significantly less expensive – giving more hospitals and clinics access to top medical devices.)

By analyzing data from sensors and other sources, generative AI algorithms can identify potential problems before they occur, allowing for preventative maintenance to be performed. This can help reduce equipment downtime, prevent equipment failures, improve health outcomes, and lower maintenance costs.

For example, Philips Healthcare developed an AI algorithm that can predict when a CT scanner is at risk of failing or shutting down, allowing for the right preventive maintenance tasks to be performed before a breakdown occurs.

Did you know that generative AI can help doctors develop personalized medicine, which tailors treatment plans to the individual needs of each patient?

Through robust data analysis, including medical records, genetic data, and environmental factors, generative AI is able to identify patterns and relationships that can predict how a patient will respond to different treatments. (Again, amazing stuff!) This lowers the risk of adverse reactions and improves treatment outcomes.

This is more than just theory – it’s already happening in the medical care industry. Researchers at the University of California, San Francisco, for example,  were able to build out an AI algorithm that’s capable of predicting which patients with heart failure are most likely to benefit from a particular type of medication.

There’s also a place for ChatGPT in drug discovery, where it can help accelerate the development of new drugs and therapies.

By analyzing vast amounts of data, including scientific literature, clinical trial data, and genetic data, ChatGPT and generative AI algorithms can identify potential drug targets and develop new drug candidates.

This can significantly reduce the time and cost of drug development, streamlining a traditionally lengthy and expensive process.

ChatGPT and Generative AI have been used in programming medical robots, including surgical robots, for a number of years to perform a range of tasks such as surgery, patient monitoring, and drug delivery.

These advanced algorithms, including ChatGPT, can analyze data from sensors and other sources, enabling the robots to make informed decisions and adapt to changing conditions.

In fact, a team of researchers from MIT, Boston Dynamics, and Brigham and Women’s Hospital has developed a medical robot that, with the assistance of ChatGPT and Generative AI, can remotely measure a patient’s vital signs to prevent doctors from being exposed to potentially harmful viruses and illnesses.

Generative AI also has a role in accelerating the clinical trial process, which can help bring new drugs and therapies to market faster than ever before. AI algorithms can mine clinical studies for potential biomarkers and patient populations that are most likely to benefit from a particular treatment.

This accelerates the timeline and lowers the cost of clinical trials (which can be in the millions of dollars). The cost has, traditionally, been a major barrier to drug development. Generative AI allows us to imagine a world where this is no longer the case.

Despite the potential benefits of generative AI in healthcare, there are also some challenges and potential risks that need to be addressed. One of the biggest challenges is the quality of data that is used to train AI algorithms.

As has been the case for decades with AI, the original data serves as the foundational groundwork. Bad data in, bad data out. Good data in, better data out. Inaccurate or biased data can lead to inaccurate or biased results. (Pretty self-explanatory, right?)

Unfortunately, in the world of healthcare, bad data can mean serious negative consequences for patient outcomes. Plus, there are concerns about privacy and data security, as healthcare data is often highly sensitive and subject to strict regulations.

Another challenge is the need for human oversight and intervention in medical diagnosis. (And, if we’re being honest, this is a really big one.) While AI algorithms can absorb and sort vast amounts of data – ultimately identifying patterns and relationships – they cannot replace the expertise and judgment of human experts.

Medical decisions should always be made in consultation with a qualified healthcare professional who can interpret the results and make informed decisions about treatment options.

There’s also the potential for legal and ethical issues related to the use of AI algorithms in clinical decision-making by medical professionals and healthcare workers.

As AI algorithms become more prevalent in healthcare, there is a risk that they may be used to make decisions that have a significant impact on patient care, such as determining the course of treatment or making a diagnosis.

This raises significant legal questions – as well as ethical questions – about which party is responsible for decisions and negative health outcomes being made by AI algorithms. We also have to consider how patients can be protected from potential risks and harm that stem from faulty data.

For example, if an AI algorithm recommends a course of treatment that later proves to be harmful to a patient, who is liable for the patient visits resulting in harm? Is it the doctor? The insurance company? Does the patient assume risk? There are so many factors to consider.

To address these challenges,  healthcare providers, researchers, and policymakers have to develop clear guidelines and standards for the use of AI in healthcare. This may involve developing new regulations and laws to govern the use of AI algorithms in clinical decision-making, as well as promoting transparency and accountability in the development and use of these technologies.

Ultimately, the goal should be to ensure that the benefits of generative AI in healthcare are realized while minimizing the potential risks and ensuring that patient care remains the top priority for healthcare ecosystem.

AI and automation are transforming healthcare and the patient experience. Research shows an 80 percent boost in employee productivity rates, better patient care management, and the enablement of self-service patient support.

If you’re a hospital or healthcare organization and you’re interested in learning more about how Aisera’s AI Service Desk can help you streamline your processes and break the cycle of mindlessly searching for information and data, you’re in the right place. Contact us today or book a demo to find out more!