As a global leader in health technology, Philips, or Royal Philips, has opened a collaboration programme for 19 start-ups to join the Philips incubator program.
The initiative is a global collaboration working across the Philips innovation hubs across the world with the focus on improving AI in healthcare.
The global collaboration program includes intelligent treatment for radiology, oncology and ultrasound. It also covers AI clinical decisions support tools such as image interpretation, analysis and workflow tools.
Out of 750 applicants, 19 of the most promising start-ups were selected for the incubator program.
How it works
The fast-track program takes place over 12 weeks. During this time Philips will engage with all of the early stage start-ups, which come from 14 different countries.
While AI has already proven its ability to improve patient outcomes and the efficiency of care, it is important not to lose sight of the patient-centred approach.
Accelerating breakthrough innovation
With the collaboration programs, Philips aims to speed up breakthrough innovation within the health industry. They are doing this through supporting internal venturing and external start-up engagement.
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We’re all overloaded by a lot information coming up on daily basis related to the AI. New applications, incredible results, amazing future on supporting humans vs. scary scenarios about machines taking control as well as losing jobs.
I recently joined the seminar by Politecnico Milano, one of the most valuable players worldwide in educating high tech figures in the all the field of engineering.
The view point was on EU level as well as into the Italian scenario, here is one of the outcomes.
AI project, this is the path
Identify the goal & solutions – the Business Analyst, with a deep knowledge like the AI engineer, could help on identify the possible advantages on adopting AI solutions, evaluating the valuable information by the company
Check system capacity – it depends on data volume, runtime analysis instead of background workflows, this indicates the requirements
Identify the Methodology – probably the most delicate phase of the project, here the presence of the data scientist is mandatory due to the necessary choise to do: deep learning, on-line learning, model prediction etc.
100% AGILE approach – project held adopting the waterfall methodology, because the initial results could change time by time expectations and goals
Learning by Doing – nothing has been already written on the stone, algorithm working in one scenario could not be applied everywhere
Quality of the data – they must be well structured otherwise it’s difficult to reach any possible goal
Validation – this is crucial, any output by AI analysis must be initially validated before pushing the solution in production
Other contents will come soon…