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Health systems worldwide have been struggling in recent years – with extremely long wait lists and severe staff shortages, experts predict that hospitals will need to cope with a 40% increase in demand in the next 15 years. The solution to these issues might lie in AI and Large Language Models.
AI models can understand and ‘write’ text, interpret large amounts of data quickly, and automate tasks that would otherwise be extremely time-consuming. They can also vastly improve the issue of communication, which is extremely vast in the healthcare system – AI in healthcare can summarize, categorize, transcribe, and translate wherever it is needed. It could also significantly lift the pressure off of booking and online first-response teams and improve patients’ experiences.
Nowadays, AI can also learn from a specific patient’s data, which is critical for gaining efficiency. According to Innovation News Network, ‘transformer’ models have a stage called ‘attention mechanism’ that learns how different inputs are related to each other, which could help the model understand the interactions of different drugs. Furthermore, greater digitization of medical records brought with it a number of automated rule sets that the systems apply for matters like medicines and allergies, and can even analyze the free text documents in a patient’s file and flag things that may have been overlooked.
Today’s LLMs excel at knowledge-based tasks, can easily understand a user’s intent and context, and generate good responses. The next step is to train them in reasoning tasks, which typically involve the model creating a chain of thoughts (a series of subtasks towards their goal) that can be updated as they act based on observations.
Despite fear and backlash, AI technology can be extremely trustworthy, and while clinicians and patients can always refuse to use AI and LLM systems, if the vast majority still uses them the overall process will save time among staff and improve healthcare conditions.
