computer vision: 7 proof points
Rows on the facts page whose own text or source section carries "computer vision". The product name links the page with the full text. All skills.
| Role | Product | Situation | Task | Action | Result | Year | Skills |
|---|---|---|---|---|---|---|---|
| PM/GM at LEO Innovation Lab | Diagnosis App Prototype smartphone app diagnosing skin conditions with machine learning | A moonshot: could a smartphone camera diagnose skin conditions? A 12-person team of data scientists, ML and computer vision engineers, wired for logic and certainty, in the deep uncertainty of product discovery. His first deep experience leading an ML team. | Lead the product direction, prioritisation and strategy, and get a working prototype that could be evaluated. | Worked with dermatologists to redesign closed-loop diagnosis and the ML training platform. Bootstrapped the ML engine with clinical decision trees. Designed the pipeline for data collection and analysis. Models were evaluated against a set of pre-tested images the dermatologists had all voted on. Not a medical device by design: disclaimers made clear it was not a diagnosis. Evaluated go-to-market and identified seven paths to market. | Over 80% accuracy across three skin conditions. Doubled the labelling output of the in-house image labelling platform. | Nov 2018 - Jan 2020 | AI, machine learning, computer vision, healthcare, discovery, prototyping, growth, launch, team leadership |
| PM/GM at LEO Innovation Lab | BetterVisitBot a pre-briefing chatbot for doctor consultations | Patients sometimes get intimidated by the doctor, and don't get the most out of their consultation, or forget what was said. | Prototype it with GPs. | Hid a dermatologist in an adjacent room to simulate a consultation chatbot. | Right idea, too early. Now it isn't. | 2016 to 2020 | AI, machine learning, computer vision, healthcare, patients, discovery, prototyping, growth, launch, team leadership |
| PM/GM at LEO Innovation Lab | prototyping and user interviews Ways of working across LEO Innovation Lab's product teams | Anthropologists were on staff at the lab, but product teams outsourced the investigation and got learnings second-hand. | Raise the quality bar across the lab, not just his own team, without a mandate over the other teams. | Championed design sprints and user testing. Made rapid prototyping and direct user interviews standard practice across all product teams. First product lead to present mistakes and learnings at the company all-hands. | Prototyping and user interviews became the norm across the lab. The monthly all-hands moved from vanity metrics to mistakes made and lessons learnt. | Nov 2016 - Jan 2020 | AI, computer vision, pharma, healthcare, patients, prototyping, incubation, ways of working, turnaround, 0 to 1, startup, team leadership |
| PM/GM at LEO Innovation Lab | testing beats opinions How design disagreements get settled | At Host Europe, as design manager in a PRINCE2 waterfall setup, a passionate argument with a graphic designer, each convinced their idea was right. Neither knew about testing. | A decade later, at LEO Innovation Lab, the same disagreement with a product designer: he preferred A, Tom preferred B. | They agreed to test it that afternoon. | Users preferred something entirely different, option C. The poor decision was anchoring on their own ideas and locking out better alternatives. Detaching from ego changed everything: it is not about who has the best idea, it is about creating space for learning. | 2005 and about 2017 | AI, computer vision, pharma, healthcare, patients, prototyping, incubation, turnaround, startup, team leadership |
| PM/GM at LEO Innovation Lab | Diagnosis App data pipeline Image labelling platform and data pipeline for the diagnosis app | Labelling by dermatologists was critical for model performance, and a significant bottleneck. | Get more labelled data through the system without lowering its quality. | Interviewed every dermatologist on the labelling work to understand how they actually worked, then redesigned the in-house image labelling platform around that. Designed the pipeline for data collection and analysis to feed it. | Doubled dermatologist labelling throughput. Less of their time on labelling training data, more labelled data for the models. | Nov 2018 - Jan 2020 | AI, machine learning, computer vision, healthcare, discovery, prototyping, growth, launch, team leadership |
| PM/GM at LEO Innovation Lab | Diagnosis App paths to market Go-to-market evaluation for a moonshot ML prototype in dermatology | A functioning prototype, but heavy regulatory barriers and no clear path to market. | Work out how, or whether, a moonshot prototype could reach a market. | Evaluated go-to-market systematically and mapped the options with the team. | Identified seven paths to market, with the trade-offs of each, for the lab to decide on. | 2019 | AI, machine learning, computer vision, regulated, healthcare, discovery, prototyping, growth, launch, team leadership |
| PM/GM at LEO Innovation Lab | LEO lab model LEO Innovation Lab as an operating model | LEO Pharma could see digital disruption coming to healthcare. Its answer was an arm's-length innovation lab: better to disrupt ourselves than let a competitor do it. Keeping it separate made sense: different thinking, different ways of working, experts from all sorts of fields. A true melting pot. | Build and ship ventures inside that model, and get them home to the core business. | They did. The lab was incredibly agile, with rapid learning cycles and brilliant peers, but too remote: the innovations struggled to find a home in the core business. | The lab worked in isolation; the parent ship sailed on unchanged, and the model was not sustainable. Disruption cannot be outsourced to a separate unit and grafted back on later; the parent has to change too. That conclusion took Tom to Novo to try the harder bet from inside. | Nov 2016 - Jan 2020 | AI, computer vision, pharma, healthcare, patients, prototyping, incubation, ways of working, turnaround, startup, team leadership, remote |