Generative AI in Healthcare: The next big revolution in medical care and research
Generative AI is fast becoming an essential factor in healthcare. Still, executives don’t always know how to use this technology to realize its full potential and avoid risks to their patient’s care.
We look at some of the most critical use cases in various healthcare segments and the implications and possibilities for the future.
Generative AI for Healthcare
The disparate sources of unstructured data, abundant in the healthcare sector, are now very useful assets to drive the use of generative AI.
This technology relies on Deep Learning algorithms to create new content, whether in text, audio-visual, code, etc. It can take unstructured datasets and analyze them, which is a breakthrough in healthcare operations, as it has a wealth of unstructured information such as clinical notes, diagnostic images, medical history, etc…
The introduction of generative AI can be used to take this unstructured data and use it independently or combine it with structured data such as insurance claims.
While the use of AI in the medical sector is not new, generative artificial intelligence represents a new tool that can help unlock some of the untapped potential for improvement in this industry. Whether automating the most repetitive or tedious work, improving the most error-prone processes, or modernizing the infrastructure of healthcare systems, generative AI has immense potential. That’s why healthcare executives need to consider integrating these models into their operational roadmap.
And in fact, they need to focus on the security of patient’s medical data, so a strategy must be put in place that always ensures the privacy and protection of this data.
Generative AI Healthcare Use Cases
Generative AI generates numerous use cases, and new potential ones will emerge over the next few years. Some examples are listed below.
Improving clinical outcomes
Solutions are already being developed to help providers improve clinical outcomes, from diagnosis to care delivery and patient follow-up.
Examples include Paige.AI, which integrates generative AI into its products to improve accuracy and efficacy in prostate cancer detection. In fact, they have been the first to receive FDA approval for the use of generative AI in digital pathology. They want to integrate the resulting information from patients’ electronic medical records and other clinical data.
Extraction of useful information for underwriting
Many inquiries require prior approval from the insurance company, usually manually. Generative AI improves the quality and efficiency of these interactions, as customer service specialists can quickly extract relevant information from dozens of plan types and files.
Thus, this technology can accelerate and improve the resolution of claim denials or approvals. These models can summarise denial letters, consolidate acceptance or cancellation codes, highlight relevant reasons, or contextualize and provide the next steps for handling these claims.
In line with the previous case, if we talk about the administrative side, applications that automate processes such as documentation, claims, authorizations, appeals, patient intake, etc., are already being explored.
Thanks to AI-powered autonomous typing services, providers’ time on administrative tasks is reduced.
To assist healthcare staff, digital solutions are available for patients to interact with directly. Generative AI can be incorporated into digital health services to understand better patients’ changing risk profiles, helping healthcare providers deliver more personalized care at lower cost.
For example, introducing a chatbot for counseling can offer affordable healthcare without waiting.
Still in its infancy, generative AI can help monitor patients in real-time and analyze data to generate personalized reports or lead to timely interventions before medical conditions worsen.
On the other hand, it can also make image-based solutions more accurate and transferable between different areas. Thanks to its adaptability and interactivity, it can promote preventive care or wellness through mobile apps or monitoring devices.
Generative AI is super useful in the pharmaceutical industry as it speeds up drug discovery, improving the planning and execution of clinical trials and leading to more precise therapies.
There are already companies that have been able to validate their products in a much shorter period, such as Insilico Medicine with a drug for idiopathic pulmonary fibrosis. It took only 30 months to complete the pre-clinical phase, which is much faster than average for a new treatment.
Accelerating clinical trials
In addition to drug discovery, generative AI can accelerate and improve clinical trials and precision medicine therapies.
Digital modeling of clinical trials, including synthetic control groups, has recently been validated. Tools are also being developed to help researchers extend existing drugs beyond their initial use to treat other diseases, making them more accessible.
Looking ahead, using this technology at the clinical and pre-clinical stages could accelerate access to therapies, even for rare conditions where the development of treatments is very difficult or financially almost impossible.
Applications of Generative AI in Healthcare
As the number of applications of generative AI continues to expand, organizations should begin to create a solid foundation for adoption and implementation. How? By taking the following steps:
- Explore new ways and business models to create an enterprise-wide generative AI strategy.
- Invest in data management and analytics tools to harness its power and build a robust data system.
- Use experts who can advise you on implementing and monitoring opportunities that generative AI can create for your business.
- Establish data and systems interoperability critical to AI to ensure good coordination with regulatory agencies to develop joint solutions.
PaLM of Google
One of the most important examples is Med-Palm 2, a large language model (LLM) designed and trained by Google to provide high-quality answers to medical questions.
PaLM stands for Pathways Language Model, based on the Transformer-like architecture that is much more efficient than previous ones and represents an AI model capable of handling many tasks simultaneously.
With the improved version of PaLM, the model has been trained on multilingual text and a larger corpus of different languages than its predecessor. It is, therefore, the most advanced model behind Bard.
Med-PaLM 2 is the LLM adapted to medicine that aims to be a medical assistant that resolves users’ doubts with high accuracy and has become a popular benchmark for evaluating performance in answering USMLE (US Medical Licensing Examination) type medical questions. In fact, it is the first AI system to get a pass on this type of question, achieving a performance of 86.5%.
This system can synthesize information from images, such as X-rays or mammograms, to help doctors provide more accurate patient care. It is still in its early stages but is already opening the door to further research in the future.
AI in the healthcare sector faces ethical and legal challenges, data privacy, error accountability, fairness of outcomes, and user trust.
At Plain Concepts, we create 360º AI solutions where we accompany you in defining and implementing the strategy for adopting this technology in your company. We offer you an OpenAI Enterprise Service, where we will create a customized model for your organization, which you can train with your data and integrate it with your systems.
We ensure your data is protected, following the main rules and regulations. So you can unlock the potential of this technology, accelerate your AI journey with our experts, understand how your data should be structured and governed, explore the use cases that best fit your goals, or create a tailored plan with realistic timelines and estimates.
Transform your business with our Generative AI Adoption Framework, created with Microsoft, which will enable you to get better results at a much more competitive cost!