Generative artificial intelligence (AI) has the potential to fundamentally transform healthcare. For example, by providing doctors and other healthcare providers with tools to analyze medical data, patients can be more accurately diagnosed and offered more personalized treatment plans.
Therefore, healthcare organizations must understand and prepare for the opportunities that generative AI could have across the industry.
Here are nine uses for generative AI in healthcare:
Diagnosis and screening
AI in healthcare, combined with predictive analytics, can help detect and diagnose various diseases earlier to improve patient outcomes. AI analyzes large data sets and identifies diseases based on the data entered into the system. Generative AI enables doctors and other healthcare providers to make more timely and accurate diagnoses and develop treatment plans for their patients more quickly, resulting in better patient outcomes.
Personalized medicine
Generative AI algorithms can analyze massive medical data sets to identify patterns, predict outcomes, and improve care and well-being. Healthcare providers can use these personalized medicine techniques to create informed treatment plans and follow-up care for their patients, increasing the chances of success. Using generative AI, healthcare providers can more easily communicate with patients, such as via email and SMS, to help them adhere to their prescriptions and/or treatment plans. Personalized medicine can not only lead to better outcomes but also reduce the overall cost of healthcare.
Higher enrollment numbers
By offering useful information and timely reminders, generative AI in healthcare can encourage more people to enroll in health insurance plans, especially during open enrollment periods. For example, by providing information about changes in policies or necessary steps that insureds need to take, generative AI can increase the engagement of insureds and encourage them to take necessary steps promptly.
Additionally, as generative AI enables insurers’ healthcare teams to quickly generate texts, they can create different versions of their policies tailored to different consumer segments. For example, workers nearing retirement need different options than workers with young children.
Drug discovery
Generative AI algorithms can analyze data from clinical trials and other sources to identify possible targets for new drugs and predict the likely most effective compounds. This could speed up the development of new medicines and bring new treatments to market more quickly and at lower costs.
Ability to interpret unstructured medical data
Unstructured medical data such as electronic medical records, medical notes, and medical images, e.g. Some data, such as X-rays and MRIs, have gaps in analysis and need to be converted into a structured format. Generative AI is capable of recognizing and analyzing unstructured data from various sources and converting it into a structured format to provide healthcare providers with comprehensive insights.
Predictive maintenance
Hospitals and other healthcare facilities can use generative AI to predict when medical devices might fail so they can better manage their maintenance and repairs, reducing equipment downtime.
Medical robots
Hospitals are using AI-driven medical robots to help with surgical procedures, such as: B. in suturing wounds and providing insights into surgical procedures based on medical data. Medical facilities can train these robots using generative AI to interpret health conditions.
Develop new ideas for research
Generative AI in healthcare can also be used for research ideas. For example, users can use ChatGPT in healthcare to generate ideas by asking questions and getting ideas immediately or simply entering a desired topic. For example, a user might ask, “Which medications have a higher chance of curing migraines?”
Avoiding medical errors
Generative AI is capable of correcting errors in documentation, automatically correcting spelling errors, which is helpful with electronic prescriptions, and ensuring that the correct data is entered into the system.
Challenges of generative AI
While there are many benefits to using generative AI in healthcare, it also has some potential drawbacks.
For example, in healthcare, generative AI is used to create synthetic images, videos, and audio files. However, it is often difficult to distinguish this generated content from real content, leading to ethical issues since generative AI can manipulate real health data.
Additionally, patients use generative AI tools to ask questions, communicate, and learn more about their medical issues. For this reason, users of generative AI tools must decide how accurate and truthful the information generated is, as it can be difficult for AI to keep up with the latest data. And when patients are provided with inaccurate information, it can lead to confusion and harm to their health.
The use of generative AI in healthcare also raises questions about securing sensitive medical patient data and protecting patient privacy. There is also the possibility that someone could access this health information without authorization and potentially misuse it.
Generative AI algorithms can also be susceptible to bias and discrimination, particularly when the algorithms are trained on health data that does not represent the population for which the data is intended. This may result in inaccurate diagnoses and/or treatment plans for the target population.
Additionally, if not used correctly, generative AI algorithms can make incorrect or harmful medical decisions. And health care providers who rely too heavily on these algorithms may be unable to make their judgments.
Because of its ability to generate images, text, audio, and more, the use of generative AI in healthcare will continue to grow and transform the way patients and providers perceive healthcare.