05a Health

On January 8th, OpenAI introduced ChatGPT for Healthcare, a generative AI (GAI) platform designed to be embedded within medical systems platforms and daily workflows. This technology suite is advertised as a solution to clinicians overburdened by administrative work through offloading cognitively taxing tasks, including the choice of diagnostic tests, supporting differential diagnosis, treatment planning, documenting session notes, creating aftercare plans for patients, and generating referral notes and discharge summaries for external providers. In other words, GAI is being implemented at every level of patient care. According to the American Medical Association’s report from their summit on AI, “disruption” of the status quo in healthcare delivery due to GAI technologies “seems inevitable.”

But why does it seem inevitable? An evidenced-based approach to evaluating new technologies would call for careful consideration of benefits and risks for technology implementation on individual use cases — not a rapid systems overhaul. Here, we must recognize that GAI technologies are products — and these products are being actively promoted to healthcare industries and healthcare professionals across the medical space, including in mental health care. Rather than investing billions of dollars into curtailing a failing system of private medical care — which has led to widespread clinician burnout and poor client outcomes — Silicon Valley companies have begun attempting to mud over these fault lines with a quick-drying GAI compound. Even the most well-meaning and justice-oriented clinician is not immune to the tidal wave of billion-dollar marketing strategies bent on creating the illusion of inevitability.

Calls for governments to push “pro-worker AI” sound appealing. The idea is simple: If policymakers deftly guide how the technology develops, they can make sure it helps workers instead of replacing them. What’s not to like?

Here’s your trouble: Technology almost never works that neatly. Its effects on jobs are usually messy, unpredictable, and shaped by millions of decisions from businesses and entrepreneurs—not by a policy plan designed in Washington.

That’s a core point in a recent critique by economist Joshua Gans of a proposal from Daron Acemoglu, David Autor, and Simon Johnson to steer AI toward worker-friendly uses. Gans says the idea runs into a basic contradiction. The proposal defines “pro-worker” technology as something that makes human capabilities and expertise more valuable. But those things are valuable partly because not everyone has them. If a new technology spreads skills more widely, it may help more workers overall—while at the same time reducing the pay advantage of those who once had rare skills.

Sam Altman is pushing back on mounting criticism over the environmental toll of A.I. The OpenAI chief has dismissed claims about A.I.’s water consumption as “fake” and drawn comparisons between the electricity required to power A.I. systems and the energy it takes to develop human intelligence.

Figures suggesting that tools like ChatGPT consume multiple gallons of water per query are “totally insane” and have “no connection to reality,” Altman said in a Feb. 20 interview with The Indian Express on the sidelines of the AI Impact Summit in New Delhi. Last year, Altman claimed that ChatGPT uses 0.000085 gallons of water per query—roughly one-fifteenth of a teaspoon—though he did not explain how he calculated that figure.

A.I.’s water footprint largely stems from the need for evaporative cooling systems used to keep data center hardware from overheating. But Altman argued that companies like OpenAI are no longer directly managing such cooling processes. Many A.I. developers, he noted, are shifting toward cooling systems that recirculate liquid rather than continually drawing fresh supplies. Meanwhile, tech giants like Microsoft, Meta, Google and Amazon have pledged to replenish more water than they withdraw by 2030.

More and more people have been experiencing psychosis induced by AI chatbot use. This is concerning since chatbot use is so prevalent, especially among young people and those who are in distress and vulnerable (one recent study found that about a quarter of young adults used chatbots specifically for mental health advice).

Reassuringly, psychiatry’s stance is that anyone who experiences this was already “prone to psychosis”—that the chatbot simply triggered delusions that would have been triggered some other way. Yet there is no evidence to support this explanation, and the case reports of those who have experienced AI psychosis tell a different story.