Generative AI: The New Lifeline to Overwhelmed Healthcare Systems
The world’s population is growing and aging, and healthcare systems are struggling to keep up. According to the World Health Organization, there are not enough health workers to handle the rising caseload, and the increased stress and burnout are pushing many to exit the field. This is leading to a diminished quality of patient care.
Understanding the Potential of Generative AI
Generative AI is not new in healthcare, but its potential is still largely untapped. Organizations have been experimenting with predictive and computer vision algorithms, but generative AI can do more. It can learn from data and create new content, such as text and images, to augment certain aspects of the healthcare system.
One of the most feasible applications of generative AI is in AI assistants for medical guidance. After COVID-19, remote consultation services became popular, but they left physicians overworked. With generative AI, healthcare organizations can launch LLM-backed AI assistants to address this. These assistants can take basic medical cases and guide patients to the best treatments based on their symptoms. If a case appears more complicated, the model can redirect the patient to a doctor or the nearest healthcare professional.
Another application is in agents for streamlining administrative work. Generative AI chatbots can handle basic healthcare operations like booking appointments and reminding patients about their scheduled visits. This can save hours of human operators’ time.
Data retrieval in workflow is another strength of LLMs. They can be enhanced with retrieval augment generation (RAG) to tap additional data resources without retraining. This enables healthcare organizations to build internal smart assistants or search systems that could provide the most relevant, contextual answers for any given query.
Data analysis and report generation are also promising applications of generative AI. With a gen AI-driven approach, teams could fine-tune models like GPT-4 vision and use them to study and generate reports from medical data, automating and accelerating the entire process.
Finally, generative AI can help with drug development. The technology can understand intricate patterns and structures in complex medical data and come up with new combinations of chemicals or novel molecule structures that could lead to potential drug candidates.
Healthcare systems are struggling to keep up with the growing population
“The current number of health workers, including physicians, radiologists, and other professionals, is not sufficient to handle the rising caseload.” - World Health Organization
AI assistants can guide patients to the best treatments based on their symptoms
Generative AI can automate and accelerate data analysis and report generation
However, caution is a must. Healthcare organizations must understand that generative AI will be only as good as the data it has been trained on. If the data is not prepared well or carries any kind of biases, the outcomes of the models will also reflect those problems.
Generative AI can help with drug development
“Generative AI can understand intricate patterns and structures in complex medical data and come up with new combinations of chemicals or novel molecule structures that could lead to potential drug candidates.”
