AI-Enabled Medical Detection Feasibility

AI-Enabled Medical Detection Feasibility

The problem

A public health organization was evaluating an AI-enabled medical detection device — promising clinical outcome, plausible technology, but a long, expensive, and regulated path between concept and deployment. Leadership needed a clear-eyed answer to a simple question: is this worth building, and what does the path look like before we commit capital?

The risk in this kind of decision is hand-wavy optimism. The cost of a bad answer is years of investment going down a path that wasn't going to work.

The approach

A focused feasibility-and-innovation strategy engagement, structured to derisk the decision before capital commits.

• Clinical-utility framing — what problem does the device solve, for whom, with what alternative today, and what's the realistic clinical lift.
• Regulatory pathway analysis — mapped the relevant approval pathways across target markets, the evidence each requires, and the realistic timeline.
• Technical feasibility — assessed the AI/ML approach, data availability, and model performance bars given the regulatory context.
• Phased build plan — three-stage build sequence with explicit decision gates, so future capital releases against demonstrated progress instead of optimistic timelines.
• Investment thesis — the financial framing executives could actually take to the board.

The impact

• A regulatory-mapped pathway, with timeline and evidence requirements per market.
• A 3-stage build plan with clear decision gates, replacing the all-or-nothing investment decision.
• An investment thesis the leadership team and board could act on.

The organization moved forward with eyes open — and the framing to manage the program against milestones instead of hopes.