Generative AI

Generative AI (GenAI) is a type of artificial intelligence (AI) that can create new data, such as images, text, or music. It does this by learning from existing data and then using that data to generate new, similar data.

While this technology is still in its early stages of development, it has the potential to revolutionize the way we create and interact with data. As it continues to improve, we can expect to see it used in a variety of applications in the years to come.

To help ensure ChristianaCare is well positioned during this pivotal era of technological advancement, a GenAI Steer is in place to help empower caregivers, along with a corporate policy on the use of Generative AI tools for guidance.

Benefits

  • Creativity – helps us to be more creative by generating new ideas and possibilities
  • Efficiency – helps us to be more efficient by automating tasks and generating new data
  • Innovation – helps us to innovate by creating new products, services, and experiences
  • Personalization – helps us to personalize our experiences by generating content that is tailored to our interests and needs
  • Accessibility – helps to make information and experiences more accessible to people with disabilities

Challenges

  • Bias – can create bias if the model is trained on data that is bias
  • Misinformation – can be used to generate misinformation, such as fake news or propaganda
  • Privacy – can use AI models to collect and analyze personal data
  • Security – can hack AI models or use them to spread malware

Applicability to Healthcare

The potential benefits of using generative AI in healthcare are numerous and significant. Meaningful application of the technology can help to improve patient care, reduce costs, and make healthcare more accessible to everyone.

Critical to successful use is data availability (models require large amounts of data to train and data can be difficult to obtain, especially for rare diseases), model accuracy (models can be inaccurate, especially if they are not trained on enough data – this can lead to misdiagnoses or incorrect treatment plans), and regulatory approval (models that are used to diagnose or treat patients may need to be regulated by the FDA which can be a lengthy and expensive process).

  • Medical Imaging – Generative AI can be used to create synthetic medical images, such as CT scans or MRIs. This can be used to train machine learning models, to create virtual simulations of medical procedures, or to generate new insights into diseases.
  • Drug Discovery – Generative AI can be used to design new drugs or to predict the effects of drugs on the body. This can be used to accelerate the drug discovery process and to develop more effective treatments for diseases.
  • Personalized Medicine – Generative AI can be used to create personalized treatment plans for patients. This can be done by taking into account the patient’s individual genetic makeup, medical history, and lifestyle factors.
  • Virtual Reality – Generative AI can be used to create virtual reality simulations of medical procedures. This can be used to train surgeons, to provide patients with a better understanding of their condition, or to reduce the need for invasive procedures.
  • Healthcare Chatbots – Generative AI can be used to create healthcare chatbots that can answer patients’ questions or provide support. This can be especially helpful for patients who live in rural areas or who have difficulty accessing healthcare.
  • Automating Administrative Tasks – Generative AI can be used to reduce the burden of administrative tasks on caregivers and patients. This can be used to transcribe and summarize patient consultations, extract data from medical records, streamline scheduling, and create treatment plan recommendations.