AI can create infinite possibilities. A single successful AI solution may even improve healthcare for the whole world. This is the value of our job.

I’m Malaysian. From UK to China, I have been working at Philips Research for 10 years. Currently, I’m leading the Intelligent Clinical Solutions and Services team at Philips Research China. At Philips, AI R&D is divided by discipline—cardiology, oncology and mother and child care. Our team helps doctors and hospitals by allowing them to better and quickly interpret patient conditions with natural language processing (NLP) and medical imaging data. If we divide AI into three parts, the first part is about imaging, like deep learning. What we are using is the second and third parts. The two parts are about leveraging NLP to interpret texts and data to transform patient conditions, symptoms and therapeutic methods into computer language, so that machines can interpret human language, analyze ultrasound, imaging and discharge reports while providing textual description of diagnosis.

We integrate the imaging data from these three sources on the same platform (the data includes the existing guides and patients with similar conditions) and then use different algorithms to find the prediction model to support doctors’ clinical decision making. For example, the “Smart Discovery Algorithm” that we are currently developing can help doctors find relevant information. If a patient dies, doctors can use this algorithm to quickly find the fields that are closely related to the patient’s death and show them on a visualization tool. A database with a large wealth of information can also help hospitals measure doctors’ performance by looking at such information as the time of hospitalization of a patient, or schedule their surgeries, CT and MR examinations, so that hospitals can allocate their medical resources more efficiently.

Thanks to AI, a job that otherwise may take 10 years can now be completed in one month.

AI can create infinite possibilities. A single successful AI solution may even improve healthcare for the whole world. This is the value of our job. I began to lead this team in 2014. Within less than two years, my team transformed the “Innovative Natural Language” algorithm into a product that has been piloted in selected large hospitals. This algorithm can automatically extract structured data from clinical reports to enable doctors to find the needed information from so many reports for secondary analysis. Without this solution, a doctor may take six months to find the needed information from 10,000 ultrasound reports. With this innovative algorithm, he can locate the desired information from 200,000 ultrasound reports within approximately one month. By rough estimate, with AI we can now finish the job that otherwise may take a doctor 10 years if he/she adopts the traditional method. Of course, the precondition for developing these solutions is to ensure the security of patient information.

Unexpectedly, thanks to the seamless collaboration between marketing and product teams, there has been a great demand for the products we develop. At the same time, we place much emphasis on technology innovation, so Philips Research China, which was originally created as a technology backup, is growing faster than our foreign counterparts in certain respects. We also support foreign projects in addition to domestic projects. Recently, we signed a successful project with the UK. Next, we are also going to partner with the US and Finland. These accomplishments will create a strong sense of belonging on our team.

Philips allows employees to make mistakes and encourages them to do things unrelated to their projects

Scientists will find certain qualities of Philips very attractive. Inclusiveness is reflected in every aspect of the company, whose flexible policies give employees much autonomy and leeway to stretch their creative imagination. When encountering issues, they can also receive timely support from their colleagues and managers. This is really a good balance. Philips also allows its employees to make mistakes, which is critical for scientists, as their creativity will not be stifled.