🌐 Deep Dive from ISPOR: The Role of AI in Healthcare Decision-Making 🌐
At our ISPOR roundtable, we explored the growing importance of AI in market access and HEOR. AIs, especially large language models (LLMs), are exceptional at making predictions based on vast datasets. They simulate understanding by predicting the next word in a sequence, next sentence in a paragraph, but they don't make judgments.
This distinction between AI's prediction capabilities and human judgment is crucial. While AIs process and predict data at an unprecedented scale, the human mind performs analysis, prediction and judgment nearly simultaneously, bringing ethical and practical consequences into play. This fundamental difference underpins why human oversight remains indispensable as we harness the strengths of these predictions.
In our session, we discussed the necessity of keeping a 'human-in-the-loop' to leverage AI's predictive power responsibly. We emphasized that while AIs can process information and suggest outcomes based on patterns, humans must remain accountable for interpreting and acting on these outcomes, considering broader implications and ethical dimensions.
Furthermore, our conversation highlighted the rigorous training required for these models to perform effectively in specialized fields like cancer genomics. Just like training a new graduate, developing an AI model involves intensive guidance, examples, and feedback—often through methods like adversarial and reinforcement learning, where models learn from their mistakes in a controlled environment.
As we continue to integrate AI into healthcare, it's critical to maintain this balance—utilizing AI’s robust data processing and predictive capabilities while ensuring that final judgments are informed by human insight and accountability. This approach will maximize AI’s potential in improving patient outcomes and operational efficiencies in healthcare settings.
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