Personalization has taken over many industries. Customers expect it when shopping online or browsing social media, and custom pricing has become the norm in insurance. These applications can save people money and are convenient, but in health care, personalization could save lives. It’s a promising step forward, though making it practical has proved difficult. Now, artificial intelligence (AI) and advanced diagnostics could finally make it a widespread reality.
The promise of precision medicine
Choosing treatments based on patient-specific factors like genetic biomarkers or personal health histories has many benefits. Some drugs or procedures may work well for some people but not others, as everyone has a unique biology. Consequently, going past a one-size-fits-all approach can significantly improve patient outcomes and reduce side effects.
Despite these benefits, precision health care has had a slow start. The idea of personalized medicine emerged in the late 90s, but fewer than five personalized drugs were available nearly a decade later. Since then, things have taken off, with personalized pharmaceuticals accounting for more than a third of all drug approvals in six of the last seven years.
While medicines targeting specific biomarkers have become more common, custom treatments remain relatively rare. There’s still a lot that researchers don’t know about the human genome. Learning enough about a patient to create a custom treatment plan also takes considerable time and may be prohibitively expensive. That is, until the AI revolution.
Personalized medicine today
Machine learning offers the analytical power hospitals and drugmakers need to unlock precision medicine. One AI model called Evo can predict how DNA changes affect organisms and even generate entire genomes. That kind of insight could streamline research into how genomic treatments could fight different conditions in different patients.
Another 2024 study created a generative AI model that can design DNA to switch on genes in specific cells but not elsewhere in the body. Those small tweaks to cells could theoretically prevent disease-causing mutations in at-risk patients or make someone’s biology more receptive to certain treatments.
In other areas of the field, AI could analyze historical records to reveal how different medical histories or backgrounds affect someone’s likelihood of developing some diseases or their treatment responses. Uncovering these trends would help doctors make more informed decisions about how to treat people without trial and error.
Remaining obstacles
Breakthroughs like these make it seem like AI has finally made personalized medicine practical. However, the industry has some work ahead of it before advanced diagnostics can rewrite the health care playbook.
Like all new technologies, AI and precision medicine are expensive. While improvements like reducing false positives can save over $202 million a year at a single facility, eventually making up for the initial cost, the upfront price is still an issue. Until these solutions become more affordable, it could mean only the wealthiest areas and patients can benefit from personalized treatments.
Machine learning is not always trustworthy, either. As powerful and accurate as it can be, mistakes are still common, especially when the data is of questionable quality. Some research shows hallucination rates can be as high as 79%, which could have big consequences when health is on the line.
Data privacy also poses a potential threat. These models need a lot of personal information to be accurate, which could make them ideal cybercrime targets. Regulations will need time to catch up to this innovation, too, which could disrupt its rollout.
AI is turning medical dreams into reality
AI-driven personalized medicine may take a while before it’s ready for large-scale adoption. However, it has already made tremendous progress. As research and development in this technology rise, precision treatments could start to unlock new standards of health care.
Machine learning will never replace human doctors. However, it could be a remarkable tool to help health experts make better decisions and offer more effective treatments. What was once only theoretical is slowly becoming a reality.
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