Artificial intelligence hiring tools are playing a growing role in screening job applicants, but one Ivy League medical student has said a flaw in that process nearly derailed his path to residency.

What happened?

As Wired reported, Chad Markey, a 33-year-old medical student at an Ivy League school, applied to 82 residency programs during the 2025-2026 cycle and was stunned by how many rejections he received.

On paper, Markey appeared to be a strong candidate. He had high grades, more than 10 published research papers, and strong recommendations from professors.

However, his application also included three medically necessary leaves of absence related to ankylosing spondylitis, an autoimmune disease that can cause severe inflammation and pain. 

Those absences were reportedly labeled as "voluntary" in a way that may have been interpreted negatively by AI screening software.

"I crawled out of a f****** black hole," he told Wired. "I could not walk for six months. I've come this far, and this is happening?"

Markey eventually began investigating whether an application review platform called Cortex was filtering him out.

The software, developed by Thalamus, is used by around 1,500 medical residency programs across the United States. Cortex is designed to process large numbers of applications and organize candidate material in a format that hospitals can review more efficiently.

After conducting his own experiments and attempting to reverse-engineer the system, Markey found evidence suggesting that applications listing voluntary leaves of absence were ranked lower than applications providing more detailed context.

Of the 82 residency programs he applied to, only five said they were not using Cortex.

The strongest indication that something may have gone wrong came when Markey began contacting residency administrators directly. Those conversations ultimately led to 10 offers from prestigious hospitals that had not materialized through the standard application process.

Markey is expected to begin a psychiatry residency at NewYork-Presbyterian Hospital affiliated with Columbia University in July.

Why is this important?

The story highlights how AI can reshape hiring decisions even without directly replacing workers.

If automated screening systems unjustifiably filter out qualified applicants before a human ever reviews their materials, the technology can reinforce or worsen barriers for people with disabilities, medical histories, employment gaps, or other nontraditional backgrounds.

That concern is especially significant in medicine, where residency placements serve as a critical gateway into the physician workforce.

If software unfairly screens out strong applicants, hospitals may lose talented future doctors while worsening bottlenecks in a field already facing staffing shortages and burnout.

The case also suggests that automated efficiency processes can come at the expense of context. A machine may flag a keyword, category, or resume gap without understanding the human circumstances behind it. In high-stakes areas such as health care, education, employment, and housing, those kinds of mistakes can have life-changing consequences.

What's being done about AI hiring tools?

Scrutiny of AI in hiring systems is increasing as researchers, advocates, and regulators push for more transparency around how automated tools evaluate candidates.

AI can still provide meaningful benefits, including reducing administrative workloads and helping organizations process large volumes of information more efficiently. But it can also make costly mistakes, and the data centers that the technology relies on produce excessive amounts of planet-warming pollution and drain local water sources. 

But cases like Markey's highlight the risks of relying too heavily on automated systems without adequate oversight.

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