As of 2024, computers do not have the ability to empathize.7 Rather, they can create words that can provoke certain sensations and sentiments among humans. When an AI chatbot tells us how important it is to take allopurinol because it cares about a patient, we should be rightfully suspicious. Humans have the ability to develop social relationships with inanimate objects, and AI has been shown to have the capacity to be manipulative and deceptive.8 Without safeguards from humans, we are inviting AI to provide sociopathic care.
Implications of AI
Beyond ethical considerations, integrating AI into healthcare is not without its economic and environmental implications. AI does not exist isolated in a box, but is rather entwined with the natural world.
Running tools powered by AI is not without costs. Although the numbers are difficult to find, one report suggests that operating ChatGPT costs approximately $700,000 per day. Electricity, water (for cooling), rental costs and more are all part of these operating expenses. Currently, these costs are somewhat subsidized by investors who anticipate their AI products will become more profitable as they become ubiquitous, but there is no guarantee this will continue. In a healthcare system that already has exorbitant costs with unclear benefits, are we ready to become one of these investors?
This cost does not even address the environmental aspects of AI. Computers and computer systems are reliant upon rare earth metals. Mining these is extremely detrimental to the environment. Additionally, to power them, electricity must be derived from sources that may not necessarily be eco-friendly. And lastly, servers need to be cooled by water, an increasingly scarce resource. Worldwide, it may require 6.6 billion m3 of water by 2027 to operate AI servers. Strategies to mitigate these negative consequences are in dire need before we implement AI within an industry that comprises 18% of our national gross domestic product.
Thoughtful De-implementation
While we rush toward the idea of implementing something new, we always have to keep our eyes on de-implementing what is old. This de-implementation must be managed carefully to ensure we don’t contribute to bloat and system redundancy. With AI updates coming at an increasingly rapid rate, healthcare leaders must prioritize plans for de-implementation of obsolete models and technologies.
De-implementation is not easy, but it is essential for diversity, equity and inclusion considerations. Innovations rarely spread in an equitable manner, but rather diffuse within communities and societies. Not surprisingly, when a resource-intensive innovation, like AI, comes to the fore the communities that do not have free resources will be the last to obtain its benefits. This is why de-implementation is important: It places the onus of healthcare equity back on those who are most eager to implement, so they can think clearly and holistically about how their additions will necessarily lead to subtraction of care from others.
