From one-size-fits-all to molecular tailoring

Medicine is evolving faster than almost any other field today. And rarely are so many players involved: laypeople, lobbyists, startups, pharmaceutical giants – everyone has something to say about health. That’s not a bad thing. After all, health is deeply personal — far more than we’ve assumed for decades. At least that’s what our author Markus Sekulla believes — and naturally, he has his own personal perspective.

In the past, we all got the same music CDs with the current hits – in my case, Bravo Hits or Ronny’s Pop Show (ppl from the 80s will remember). A one-size-fits-all selection. Today, Spotify creates custom playlists for me every day. Sure, they’re not always spot on but who am I to criticise the algorithm.

Let’s transfer that analogy to medicine: today, based on your genes, symptoms, and lifestyle – curated by doctors and algorithms – a personalised treatment plan can be created. Hopefully with more hits than Spotify.

Where once the "watering can" principle applied – give everyone the same and hope it works – modern medicine now relies on genetic analysis and fine-tuned precision. A kind of molecular tailoring. This approach is already well-established in cancer medicine: instead of “breast cancer is breast cancer,” the tumor’s genetic profile is analysed. For instance, the drug trastuzumab only works if a specific gene (HER2) is overactive. If it isn’t, the treatment – along with its side effects and costs – can be skipped.

Even with rare diseases, a glance at the genome can bring clarity to diagnostic mysteries. Conditions that baffled doctors for decades can now often be traced back to a genetic defect. Even with everyday medications like blood thinners or antidepressants, it’s clear: not everyone tolerates the same drugs – and our DNA often knows more about our compatibility than any package insert.

All of this was made possible by a quiet revolution: gene sequencing. What cost around 100 million dollars at the turn of the millennium – about as much as a well-equipped high-speed train car – now costs less than $1,000. Genetic analysis has gone from a research luxury to a clinical routine.

AI as a lifeline in the healthcare fata flood

More data means one thing above all: more complexity. A single genome generates several gigabytes of information – and that’s just the beginning. Combine that with blood values, MRI scans, patient records, and the latest sleep score from your fitness tracker, and you’re left with a massive puzzle. Way more than any human brain could handle, even with decades of experience. The lifeline: AI.

Algorithms detect patterns, assess risks, and can suggest treatment options. In radiology, AI can spot microscopic changes invisible to the human eye. In pathology, it supports the diagnosis of rare tumors. In the future, it may even predict which medication will work — before any doctor can. We’re still at the beginning, and the usual gap between early and late adopters remains.

After diving deep into the topic, here’s the one thing I know for sure: the potential of AI is greater than we can imagine today — which is both a thrilling and unsettling thought.

But the more precise medicine becomes, the bigger the questions grow. Who should be allowed to know what my genes reveal about me? Do I really want to know if I have a higher risk for Alzheimer’s, when there’s (currently) no treatment? And what happens if this information is not just known to my doctor, but also a tech giant from Silicon Valley?

That’s not the only issue. Studies for highly specialized drugs often involve small sample sizes – traditional approval procedures reach their limits. And tests that determine whether a drug is even effective must meet the highest quality standards – otherwise, you might end up treating a gene variant that doesn’t even exist.

And then there’s the usual suspect: Money. Many of these treatments are expensive – sometimes reaching six figures annually. Critics ask: how can a solidarity-based healthcare system afford that? Supporters counter: the earlier and more targeted the treatment, the more costly trial-and-error treatments can be avoided. And who says the current status quo – treating people with ineffective therapies – is cheaper?

A long journey – destination unknown

Looking ahead, preventive genetic tests could become as routine as a check-up at your GP. Wearables might send real-time body data, which AI could evaluate to offer recommendations – maybe even suggest treatment adjustments before symptoms appear. We’re already seeing this with diabetes, where sensors and pumps work together to regulate blood sugar levels.

A particularly exciting approach is digital twins – virtual, data-based replicas of people used to simulate how a treatment would work, without the person actually having to undergo it. I just hope these twins won’t tell us bad news about our future before we’re ready.

Tl;dr

Personalised medicine is more reality than science fiction. As in many areas of 2020s life, the technical foundations are improving rapidly – we just need to make sure we, as a society, keep up.

Further Reading

https://www.vfa.de/de/forschung-entwicklung/personalisierte-medizin

https://www.fda.gov/medical-devices/in-vitro-diagnostics/precision-medicine

https://www.computerwoche.de/article/2782234/mit-ki-und-big-data-wird-die-medizin-smart-und-personalisiert.html