Generative AI in the Healthcare Sector
The healthcare industry is one of the top producers of big data given the increasing digitalization and adoption of information systems within the industry coupled with the complexity and volume of data generated throughout the care delivery process.
A rising need of quality healthcare facilities, under-staffed hospitals and long timelines for the development of drugs and vaccines necessitate a more efficient healthcare ecosystem and AI, specifically generative AI, will play a big role in achieving the same.
Why the healthcare ecosystem is paying attention …
COVID-19 showed the world that we will not always have years to develop drugs and vaccines. It also highlighted the mismatch between the population and the size of the healthcare workforce. With the large amounts of data available, AI can hasten and automate numerous sub-processes leading to better health outcomes.
The healthcare industry’s collaboration with AI has been established by a growing demand for NLP-based voice recognition software that can automate patient data entry, improving productivity and cash flows. Microsoft’s $20 billion acquisition of healthcare focused voice AI solutions provider, Nuance Communications is a testimony to the growth opportunity that the healthcare vertical offers to AI-based technologies.
While AI in healthcare has been around for a while, generative AI, with its deep learning techniques and advanced machine learning models, has been generating significant interest from medical practices, professional organizations as well as medical journals leading to the creation of task forces to probe how generative AI might be useful and to understand what limitations and ethical concerns it may bring.
According to a study by Ansible Health, ChatGPT demonstrated that it could probably pass the medical licensing exam.
In Feb-23, ZS, a global management consulting firm with deep expertise in the healthcare vertical, announced its acquisition of Trials.ai, an AI-powered clinical study design company to help clinical development teams devise smarter studies and reduce the time to launch new pharmaceutical products.
In Nov-22, Syntegra, a leader in generating synthetic healthcare data, used generative AI to create synthetic datasets while maintaining the statistical fidelity of the real data, including rare cohorts and outliers, and fully protecting patient privacy and complying with HIPAA and GDPR standards.
Potential near-term use cases:
Drug discovery and development: Analyze historical data and identify potential combinations of compounds which are most likely to be effective
Uninterrupted medical assistance: Provide round-the-clock monitoring and create alerts for patients as well as doctors when data from the wearable devices breach ranges preset by the doctors
Personalized medicine: Create personalized treatment plans for patients considering their medical history and identifying patterns thereby increasing the chances of success and reducing the risk of side effects