DeepCare Leverages AI and Medical Imaging to Revolutionize Screening and Diagnosis of Critical Diseases

DeepCare is a technology company specializing in the recognition and screening of medical images, distinguished by its use of artificial intelligence.

Liu Sheng, Co-founder and CEO of the company, told VCBeat, “We aim to build a big data and artificial intelligence platform for medical imaging, enabling all medical hardware and software to become AI-enabled.” Specifically, DeepCare focuses on the research and development of technologies for detection, recognition, screening, and analysis of medical images. By integrating machine vision, deep learning, and big data mining techniques, the company is committed to providing portable medical device manufacturers and primary healthcare centers with fast, accurate, and cost-effective medical image recognition solutions.

In recent years, the integration of medical imaging and artificial intelligence has gained significant momentum both domestically and internationally. Against this backdrop, DeepCare has entered the market, partnering with medical hardware manufacturers to provide AI algorithms for statistical analysis and preliminary diagnosis, thereby enabling the intelligent transformation of medical devices.

According to the “2015 Annual Statistical Report on Food and Drug Supervision” issued by the China Food and Drug Administration (CFDA), as of the end of November 2015, there were 5,080 manufacturers of Class I medical devices, 9,517 manufacturers of Class II medical devices, and 2,614 manufacturers of Class III medical devices in China. Among these, more than 12,000 companies producing Class II and Class III medical devices were subject to national production licensing management. In fact, all of these enterprises are potential customers of DeepCare.

“Major equipment manufacturers such as GE, Philips, and Siemens embed intelligent image recognition technology directly into their devices. However, small and medium-sized equipment manufacturers are unable to sustain in-house deep learning algorithm teams; they must either forgo this functionality or outsource it to algorithm development firms,” said Liu Sheng. He noted that for these smaller manufacturers, finding suitable outsourcing partners is not only difficult but also costly. Typical outsourcing fees range from RMB 300,000 to 500,000. In contrast, DeepCare adopts a SaaS (Software as a Service) model, under which customers pay annually based on the modules they use. Each module costs only RMB 30,000 to 50,000. For example, cervical cancer screening comprises three modules—covering lymphocytes, epithelial cells, and red and white blood cells—making the overall cost lower than that of outsourcing companies, while also ensuring higher service quality.

Liu Sheng introduced that payment can be made in cash or offset with data. In other words, DeepCare’s customers can use its algorithm modules free of charge or at a very low cost by providing a certain volume of data annually. Currently, DeepCare has partnered with a cervical smear testing company in Shanghai, a handheld ophthalmic device manufacturer in Suzhou, a urinalysis institution in Chongqing, and a medical imaging cloud software company in Beijing, providing them with modular algorithm services.

Overall, DeepCare has two core objectives: to imbue hardware with artificial intelligence and to infuse software with artificial intelligence.

Hardware AI integration refers to DeepCare’s ability to help partner medical device manufacturers implement automatic recognition capabilities at a low cost, thereby facilitating the deployment of these devices in township-level primary care clinics. This reduces the barrier to entry and makes chronic disease management more convenient, efficient, and accurate.

The AI integration in software serves as a virtual assistant to medical experts. First, in the realm of medical imaging, AI-enabled software can currently screen CT scans, performing preliminary screenings for physicians by flagging images with suspicious lesions for each patient. Second, the software can automatically generate portions of diagnostic reports, which physicians can then refine, thereby saving time. With each refinement made by physicians, the system’s algorithms engage in self-learning and continuous improvement. Furthermore, AI-driven software helps radiologists and clinicians truly leverage data by providing “image-based search” functionality. This allows physicians to quickly utilize imaging data to identify previously treated patients with similar conditions, offering more comprehensive data analysis to support diagnostic decisions and treatment planning.

Co-founder and CEO Liu Sheng graduated from Harvard Business School. He is a former senior consultant at McKinsey & Company and a serial entrepreneur in Silicon Valley, having launched two startups in the internet and new energy sectors in Silicon Valley, USA. Co-founder and CTO Dr. Ding Peng graduated from Dartmouth College. He has worked at several leading companies in computer vision and possesses extensive research background in artificial intelligence.

In addition, other team members are experts in fields such as medical image processing and artificial intelligence, with over a decade of research experience in the field of medical imaging.

It is reported that DeepCare has secured RMB 6 million in angel-round funding from FreeS Fund, with a current valuation of RMB 50 million.