Aidoc CEO on the advantages a healthcare AI startup has over Google and IBM

Steven Loeb · April 18, 2019 · Short URL: https://vator.tv/n/4dba

The company, which provides AI for medical imaging, raised a $27M round of funding

AI seems to be having a huge impact right now on just about every space, with more data pouring in than ever before, but it can perhaps be felt most acutely when it comes to health. That's thanks, in large part, to the proliferation of EHR systems, which make it easier than ever to for data to be shared, so it can be turned into something actionable, resulting in faster response times and more targeted care. The amount of data in healthcare is growing at a rate of 48 percent annually and healthcare AI is expected to quickly grow to $8.6 billion by 2025. 

Obviously, there's a ton of potential for AI in healthcare, and investors are taking notice; from 2013 to 2018, there was more than $4.3 billion put into these companies

The latest to get funded is Aidoc, a company that uses deep learning algorithms to analyze medical imaging, allowing radiologists to better detect abnormalities. Earlier this week, the company announced that it raised a $27 million Series B round, led by Square Peg Capital. The funding brings Aidoc's total raised to $40 million, and the company says it will use the funding to grow out its team, and to build out its technology, specifically with the launch of its oncology line of products.

Founded in 2016, the Tel Aviv, Israel-based Aidoc was named one of Time Magazine's 50 Genius Companies of 2018 and its founders were recognized in Forbes' "30 under 30" list. The company recently announced that it analyzed its millionth patients CT scan in real-time.

Elad Walach, co-founder and CEO of Aidoc, spoke to VatorNews about how AI is impacting healthcare, why oncology is an important next step for the company and how he plans to do up against companies like Google and IBM. 

VatorNews: What is the problem that you have identified and how are you solving it with Aidoc?

Elad Walach: The number of medical images taken at hospitals has exploded, according to one study, from 1999 until 2010, the average CT exam increased from 82 images to 679, yet the number of radiologists has remained static. As a result, critical cases can often wait an hour or more until they are seen by a radiologist.

This is where Aidoc's artificial intelligence tools are filling the gap. Aidoc can analyze hundreds of images in a matter of minutes and triage abnormalities so that radiologists can prioritize potentially critical cases and save lives.  

VN: Who is the typical customer for Aidoc? Walk me through some typical use cases.

EW: Any medical center or hospital which offers medical imaging. Aidoc doesn't aim to replace radiologists; it triages patients so that radiologists see the most urgent cases first. Aidoc flags life-threatening and time sensitive conditions like brain hemorrhages or pulmonary embolisms and prioritizes  those patients for immediate treatment.

VN: How many customers do you currently have? How are you growing?

EW: Aidoc is in use at 100 medical centers worldwide and the goal is to increase that to 500 within two years. 

VN: What kind of ROI have patients seen thanks to Aidoc?

EW: Aidoc has been shown to reduce turnaround time by 32 percent for critical cases and has a 96 percent accuracy. There are documented cases where Aidoc has already likely saved lives.

VN: How large is your team currently? How many people do you plan to add and in which departments?

EW: Aidoc currently has a team of 60 with plans to double the team within a year. The new team members will be distributed across all departments. 

VN: What are some of the things you’d like to go going forward that you haven’t been able to do so far?

EW: Aidoc will be releasing its oncology line of products as well as the extension of its current suite for time-sensitive conditions to X-ray. 

VN: Tell me about your oncology line of products. Why is this an important area for Aidoc’s future growth?

EW: Cancer is the second leading cause of death globally. In 2018, it was estimated that 1,735,350 new cases of cancer would be diagnosed in the United States and 609,640 people would die from the disease. 38.4 percent of men and women will be diagnosed with cancer at some point during their lifetimes. 

The oncology solution will automatically and instantly detect, measure and compare tumor size with past scans as soon as the radiologist opens the image. 

VN: Who are your competitors? What separates Aidoc from them?

EW: AI for medical imaging is in an interesting place. On the one hand, the field is full of tech giants. IBM's Watson and Google's Deepmind are two famous players. More recently, imaging hardware manufacturers like Samsung and Phillips are also looking at AI software too. However, focused startups have found that they have a competitive advantage, being able to develop, improve and market bespoke AI tools that are often more advanced than the big broad tech companies.

At this stage, many startups are beginning with particular detections and there is little overlap, but as these companies mature and add detections, they will begin to move into each other's spaces. Aidoc can train its AI 30 times faster than others, and they release new anomaly detection every quarter. Because it's in real hospitals seeing thousands of new patients every week, Aidoc's system is constantly learning and improving.

VN: How has AI in healthcare changed the space in recent years? What do you see as the next step in that evolution?

EW: AI in healthcare has gone from SciFi to reality and now it’s on its way to becoming the new standard of care. AI for radiology is leading the way. 

VN: What is your ultimate goal with Aidoc? Where do you want to see the company in the next 5 years?

EW: Aidoc's ultimate goal is to create the first full-body AI imaging solution. 

VN: Is there anything else I should know about the company or the new funding round?

EW: That Aidoc’s results are clinically proven and independently monitored through its partnership with the American College of Radiology. 

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