Generative artificial intelligence (AI) has the potential to be a transformative force in healthcare, for example by providing physicians and other healthcare providers the tools to analyze medical data, more accurately diagnose patients, and offer them more personalized treatment plans.
As such, it’s critical for healthcare organizations to understand and prepare for the possibilities generative AI could have across the industry.
Here are nine uses of generative AI in healthcare:
Diagnosis and Screening
AI in healthcare combined with predictive analysis can help detect and diagnose various diseases earlier to improve patient outcomes. AI analyzes large data sets and identifies diseases based on the data put into its system. Generative AI allows doctors and other healthcare providers to make more timely and more accurate diagnoses as well as more quickly devise treatment plans for their patients, leading to better outcomes for their patients. (Also Read: AI in Healthcare: Identifying Risks & Saving Money)
Personalized Medicine
Generative AI algorithms can analyze massive medical datasets to uncover patterns, forecast outcomes, and enhance care and wellness. Healthcare providers can use these personalized medicine techniques to customize more informed treatment plans as well as follow-up care for their patients, boosting the chances of success. Using generative AI, healthcare providers can more easily communicate with patients, for example via email and text. to help patients adhere to their prescriptions and/or treatment plans. In addition to leading to better outcomes, offering patients personalized medicine can also reduce the total cost of healthcare.
Increasing Enrollment
By offering useful information and timely reminders, Generative AI in healthcare can encourage more people to enroll in health plans, especially during open enrollment periods. For instance, by providing information regarding changes in policies or any necessary steps policyholders need to take, generative AI can boost policyholder engagement and encourage them to complete the steps they need to take in a timely manner.
Additionally, since generative AI enables insurers’ healthcare teams to quickly generate text, they can create different versions of their policies tailored to various consumer segments. For example, employees close to retirement need different options than workers with young children.
Drug Discovery
Generative AI algorithms can analyze data from clinical trials as well as from other sources to identify possible targets for new drugs and predict the compounds likely to be the most effective. This could speed up the development of new drugs and get new treatments on the market faster and at a lower cost.
Ability To Interpret Unstructured Medical Data
Unstructured medical data, such as electronic health records, medical notes, and medical images, e.g., X-rays and MRIs, create gaps during analysis and must be converted into a structured format. Generative AI is able to detect and analyze unstructured data from multiple sources and convert it into a structured format to provide comprehensive insights to healthcare providers.
Predictive Maintenance
Hospitals and other healthcare facilities can use generative AI to predict when medical equipment might fail so they can better handle their maintenance and repairs, reducing equipment downtime.
Medical Robots
Hospitals use AI-driven medical robots to help with surgical operations, such as suturing wounds and providing insights on surgical procedures based on medical data. Medical facilities can use generative AI to train these robots to interpret health conditions.
Developing New Research Ideas
Generative AI in healthcare can also be used to research ideas. For example, users can leverage ChatGPT in healthcare to generate ideas by asking questions and getting instant ideas or just by typing a desired topic. For instance, a user might ask “Which drugs have higher chances of curing migraines?”.
Avoiding Medical Errors
Generative AI has the ability to correct mistakes during documentation work, automatically correcting spelling errors, which is helpful for electronic prescriptions, and ensuring that the right data populates the system.
Challenges of Generative AI
While there are many advantages to using generative AI in healthcare, there are also some potential downsides.
For example, generative AI in healthcare is used to create synthetic images, videos, and audio; however, it’s often difficult to differentiate this generated content from real content, resulting in ethical issues since generative AI can manipulate real healthcare data.
In addition, patients use generative AI tools to ask questions, communicate and learn more about their medical conditions. Because of this, users of generative AI tools must determine how accurate and truthful the generated information is because AI may have a hard time keeping up with the latest data. And providing patients with inaccurate information can mislead them and harm their health.
Using generative AI in healthcare also raises issues about securing sensitive patient medical data and protecting patient privacy. And there is also a chance that someone may access this healthcare data without authorization and potentially misuse it.
Generative AI algorithms can also be susceptible to bias and discrimination, especially if the algorithms are trained on healthcare data that doesn’t represent the population the data is meant to serve. This can cause inaccurate diagnoses and/or treatment plans for the target population. (Also Read: Why Does AI Have Biases?)
Additionally, generative AI algorithms that aren’t used properly can make incorrect or harmful medical decisions. And healthcare providers that depend too heavily on these algorithms may not be able to make judgments on their own.
Because of its ability to generate images, text, audio, and more, the use of generative AI in the healthcare sector will continue to increase, transforming the way patients and providers perceive healthcare.
