The main obstacle to better antimicrobial resistance (AMR) management is not a technological deficit. Advanced diagnostics exist, but healthcare systems struggle to implement them. The key is generating real-world evidence and health economic data to convince policymakers and change clinical practice.

A funding paradox exists where capital-efficient medical service platforms struggle to raise funds while high-risk, cash-intensive therapeutic companies secure large rounds. This is because investors understand the traditional drug development model but are unclear on how to value a medical service.

For a successful drug, like a cancer immunotherapy that works on 50% of patients, the manufacturer has no financial incentive to develop a test identifying responders. Creating such a test would effectively cut their market in half, as non-responders would no longer be prescribed the drug.

Even with advanced imaging for diseases like Alzheimer's, adoption stalls if diagnostic results don't change patient management. Physicians won't use a test that answers an academic question but doesn't lead to an effective treatment, rendering the technology clinically irrelevant without answering the 'so what?' question.

A common mistake in pharma is viewing imaging data solely for its diagnostic value—identifying where a disease is. The greater, untapped potential lies in its predictive value. When made computable, imaging data contains signals about how a disease behaves and will respond to specific treatments, making it a powerful predictive asset in oncology.

For decades, there was little focus on Alzheimer's diagnostics because a diagnosis offered no effective treatment. The recent emergence of disease-modifying therapies has created an urgent, market-driven need for accurate and accessible diagnostic tools, demonstrating how therapeutic breakthroughs directly fuel diagnostic innovation.

Gaining FDA approval is not the finish line. Many innovative devices fail because they lack a clear reimbursement strategy. Founders must build the economic case for payers and providers in concert with their clinical and regulatory strategy from day one.

In healthcare, the user, recommender, and payer are often different entities. A clinically effective product can easily fail if it's not inserted into the right point in the value chain where a stakeholder is both willing and incentivized to pay for it.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.