Diagnostics is not limited by the tests we have. It is limited by how we use them.
Clinicians are routinely faced with a complex question: what is the best next test for this patient? The answer depends on far more than test accuracy alone. Cost, turnaround time, patient context, and system constraints all play a role. This makes optimal decision-making incredibly difficult in practice.
In this conversation, TTP’s Giles and Jamie explore how AI could be applied not to improve individual tests, but to guide entire diagnostic pathways. They introduce the concept of Dr DAIsy, a system designed to use patient data, probabilities, and real-world constraints to identify the most informative next step in diagnosis.
The result is a shift in perspective. It moves from generating more data to extracting more value from the data we already have.
Watch the full video to explore how this approach could reshape diagnostics, improve efficiency, reduce unnecessary testing, and enable better patient outcomes.
Want to explore the thinking behind Dr DAIsy in more detail?
Our white paper, Dr DAIsy - Anatomy of an artificial doctor, sets out how AI could guide diagnostic pathways by identifying the most informative next step for each patient.
Or put yourself in the doctor’s seat with Diagnose Who?, our interactive game that challenges you to navigate uncertainty and choose the best tests.
