The Life-or-Data Conundrum

Hospitals are turning to large language models to help triage patients, letting algorithms read through charts, symptoms, and fragments of medical history to rank who gets care first. In early use, the models often outperform overworked staff, catching quiet signs of crisis that would have gone unnoticed. The machine scans faster than any human ever could. Some lives get saved that would not have been.

But these models run on histories we have already written, and some lives leave lighter footprints. The privileged arrive with years of regular care, full charts, stable insurance. The poor, the undocumented, the mistrustful, and the systemically excluded often come with fragments and gaps. Missing records mean missing patterns. The AI sees less risk where risk hides in plain sight. The more we trust the system, the more invisible these patients become.

Every deployment of these tools widens the gap between the well-documented and the poorly recorded. The algorithm becomes another silent layer of inherited inequality, disguised as neutral efficiency. Hospitals know this. They also know the tools save lives today. To wait for perfect equity means letting people die now who could have been saved. To deploy anyway means trading one kind of death for another.

The conundrum

If AI triage delivers faster care for many but quietly abandons those with thin records, do we press forward, saving lives today while deepening systemic neglect? Or do we hold back for fairness, knowing full well that delay costs lives too?

When life-and-death decisions run on imperfect data, whose survival gets coded into the system, and whose absence becomes just another invisible statistic?

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The Life-or-Data Conundrum

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