Generative AI and Healthcare: An Innovative Partnership
Over the past decade, artificial intelligence (AI) has been at the forefront of innovation across industries. Its existence and evolution have resulted in a transformative impact on businesses by increasing efficiencies, reducing costs, and delivering results at the click of a button. Generative AI is a type of intelligence that can produce various outputs such as text, images, and other forms of content based on how the data is trained to learn and evolve. Moreover, this type of AI learns patterns and structures within existing data to produce requested results in record time.
While this technology has been a welcomed addition in financial sectors, academics, manufacturing, and retail, its adoption by the healthcare sector has been questioned. The healthcare industry as a whole is a complicated and highly regulated environment that makes the adoption of AI a bigger hurdle to overcome.
So, can this technology produce accurate and reliable results with patient diagnoses, drug discoveries, and clinical development? Can it be trusted by scientists, physicians, and clinical research organizations to process patient and clinical data? These are the questions the healthcare industry has been faced with in a time where the world has widely embraced AI and its many benefits. In this article, we will break down the benefits of generative AI in this sector and how it can transform the way healthcare institutions operate.
Diagnostic Efficiency
Generative AI has had a major impact in medical diagnostics. The healthcare industry has been plagued with long wait times, staffing shortages, and costly diagnosis procedures and processing methods. Medical imaging technologies, such as CT scans, MRIs, and X-rays play an integral role in diagnosing patients, and generative AI algorithms have been utilized by medical professionals to help improve the accuracy of diagnoses. This technology can take the images produced by these pieces of machinery and enhance the quality, pinpoint obscurities, and aid in the early detection of diseases, such as cancer. Therefore, this AI has been used by radiologists and pathologists to provide quicker diagnoses that can then be reviewed and vetted by medical professionals.
CT scans, MRIs, and X-rays play an integral role in diagnosing patients, and generative AI algorithms have been utilized by medical professionals to help improve the accuracy of diagnoses."
This technology also uses generative adversarial networks (GANs), which utilizes competing models to produce synthetic data by creating two algorithms, a generator, and a discriminator. The generator creates fake images and overtime produces more realistic data, while the discriminator aims to determine if the output of the generator is real or fake. Overtime, the AI learns and evolves to create generator data that is so realistic, the discriminator cannot disprove it, resulting in more accurate and definitive diagnoses. In summary, the mechanics of generative AI is intended to constantly improve itself until it can only produce near perfect conclusions.
Research and Literature
Everyone is aware of the time and concentration it takes to make it through a textbook during their years in academia. Historically, mankind has relied on human interpretation of texts to learn and develop knowledge of various subject matters. With generative AI, Natural Language Processing (NLP) can analyze thousands of pages of text and data, extract insights, and produce analyses in a matter of seconds. This can aid researchers, clinicians, and scientists in generating hypothesis, and even develop patient care plans, clinical study strategies, and general white papers in record time. In the end, this vastly cuts down on the hours it takes to produce content and gives back time to these individuals to focus on their more important tasks.
Operational Efficiencies
Healthcare providers are faced with several challenges from an administrative standpoint. These challenges include scheduling patient care, monitoring and maintaining paper or electronic patient records, and processing patient data, such as billing and insurance information. Generative AI utilizes chatbots and virtual assistants to handle monotonous work which cuts down on human error and frees up employees to concentrate on more important tasks. This also plays a large role in reducing labor costs and improving the bottom line.
Moving Forward
Despite this evolution in AI, nothing will ever replace the trusted relationship between patients and doctors, and the brilliant minds behind scientific discovery and innovation. However, generative AI can continue to be a useful tool for both life science companies and healthcare providers to cut down on diagnostic times, eliminate certain operational activities, and reduce labor costs.
Consequently, generative AI models have begun pilot programs in hospitals and contract research organizations across the country. The AI boom has secured massive investments from top technology companies, such as Amazon, Microsoft, and Google. The goal of these companies is to tap into highly regulated industries, including the healthcare sector to solve some of its most oppressive problems.
Although implementation is still in its early stages with pilot programs, module testing, and case studies, it is apparent that this groundbreaking technology is here to stay. Over time, generative AI will continue to evolve, learn, and perfect itself to be a valuable asset to the healthcare industry and its capabilities are just beginning to be discovered.
At Wolf, we offer industry expertise in healthcare, technology, and more – reach out to a member of our team to learn how we can help you thrive in this evolving landscape.
