EXAID applies cutting-edge deep learning algorithms to analyze medical images.

EXAID is a decision support tool that provides physicians with unparalleled disease detection accuracy, thereby reducing image reading times and providing actionable insights on treatment options.

Apart from insights, we establish trust between physicians and our AI tools by letting machine explain its decision-making process, thereby enabling physician to make an educated decision.

Problem

Medical misdiagnosis is the third leading cause of deaths in the US
10-20% of all cases are misdiagnosed, leading to ~12M misdiagnosis in the US/year
Medical errors cost $19.5bn/year in the US

Cancer misdiagnosis is more common
Prostate cancer tests miss severity in 50% of cases
Pancreatic cancer misdiagnosis rate is 31%

Doctor to Patient ratio : 2.5 doctors / 1000 people in the US

Medical imaging technology has greatly advanced over the years providing doctors with the tools they need for diagnosis. However, very little has changed with respect to the clinical decision support systems.

Clinical decision support systems currently being used rarely take cues from multiple information sources (multimodal data).

Doctors lack trust in the decision’s provided by machines, due to which mass adoption of clinical decision support systems is yet to happen.

Existing AI tools suffer from the “Black Box” problem where the user is unable to see how the inputs are being analysed by the algorithm.

“If we wish clinicians to place their trust in a computerized decision aid, then we will need to figure out how to build such trust” David A. Cook, Mayo Clinic [1]

Solution

Give physicians the ability to detect diseases with an unparalleled accuracy, reducing the time the physician spends reading the image and helps provide actionable insights on treatment options.

Establish trust between physicians and our AI tools by letting the machine explain its decision-making process, thereby enabling physicians to make an educated decision.

EXAID’s interface and information architecture unlocks the “Black Box” and shows how the model came to its conclusions.

Streamlined records from across various hospitals now feed into EXAID making the matches significantly more accurate.

Weighted scales allow the physician to adjust the weight of each field for greater accuracy and specificity.