Rare diseases: AI fills in for scarce scientists where humans fall short

While the world argues over whether ChatGPT will replace copywriters and programmers, Web Summit in Qatar is discussing something far more vital. For decades, treating rare diseases has hit the same wall: harsh economics and an acute shortage of human talent. There are billions of us, but very few scientists are able to spend years searching for a formula for a disease that affects three people in a million. This is where artificial intelligence stops being a toy for generating images and becomes the "extra pair of hands" medicine has lacked since its beginnings.

Let's be honest: traditional pharmacology is insanely expensive and slow. Finding a new drug used to resemble hunting for one particular needle in an endless haystack while blindfolded. Scientists sifted through thousands of molecules by hand, hoping to get lucky. Today, biotech startups are showing how algorithms do it in a matter of weeks. AI doesn't just "think" faster than a human; it automates the routine that once required hundreds of lab technicians and millions of hours of work. This changes the rules entirely, especially for so-called "orphan" or rare diseases, which big pharma was reluctant to tackle because of the staggering costs.

The pairing of AI with gene-editing technologies such as CRISPR is particularly interesting. Together, these tools let scientists predict the consequences of DNA changes before they ever touch a real test tube. We are rapidly moving from an era of trial and error to one of precise engineering calculation. Where one stage of research once consumed years of an entire institute's work, a single experienced bioinformatician with the right software now delivers a result that is faster and more accurate. This is not magic. It is pure mathematics that has finally caught up with the complexity of human biology.

Why is this conversation happening now? The technology stack has finally matured. We have the computing power, vast genome datasets have been accumulated, and, importantly, investors have begun to understand that AI in biotech is not hype but the only way to scale medicine. Small startups are starting to compete successfully with industry giants simply because their core "staff" consists of algorithms that don't sleep, don't get tired, and don't make mistakes from inattention.

This shift toward automation is a forced measure. The global shortage of qualified scientific personnel keeps growing. An aging global population demands ever more new drugs, while the number of young scientists willing to devote their lives to the lab is not growing in proportion. In this context, AI acts not as a competitor to humans but as a powerful amplifier. It takes on the most tedious, mechanical but voluminous part of the work, leaving people the final decision and the creative scientific breakthrough. We delegate the brute-force search through options to the machine and keep the strategy for ourselves.

Ultimately, we are watching the birth of an industry in which the term "rare disease" may stop being a death sentence. If the cost of developing a drug falls tenfold thanks to total automation, it will become profitable for pharmaceutical companies to treat even patients whose diagnoses appear once a decade. That is real humanism, packaged in code and server racks. Technology finally lets us address the problems of each individual person, not just the averaged majority.

The bottom line: The shortage of scientists is no longer an insurmountable barrier to medical progress. AI turns biomedicine from an elite craft into a scalable technology. Whoever fully automates their lab first will lead the market for the drugs of the future. But will human intelligence be able to keep control of the process when discoveries start coming faster than we can grasp them?