What You Should Know:
– Kahun, an evidence-based clinical reasoning tool for physicians, announces the publication in the International Journal of Medical Informatics (IJMI) of a first-of-its-kind study assessing the data-gathering function of currently available chatbot symptom-checkers. Out of eight symptom-checkers—K Health, Babylon, ADA, Buoy, Kahun, Mediktor, Symptomae, and Your.MD.
– Kahun demonstrated the best overall performance in finding the most pertinent insights in a simulated patient conversation.
Why It Matters
Symptom checkers face scrutiny by physicians since they fail to produce an output that is professional and explainable enough for providers to trust. For this reason, many physicians are skeptical to trust symptom checkers to ease the burden healthcare providers are experiencing in a post-pandemic landscape.
Kahun’s AI-driven clinical reasoning assessment solution tackles this by looking at the challenge through the eyes of the physician. If symptom checkers act like Google, Kahun acts like your own doctor who knows which questions to ask and the physician’s thought process through its unique algorithm, and by that delivering to your physician the information like his colleague would have delivered, not Google.
Kahun recently published the first-of-its-kind study assessing the data-gathering function of currently available chatbot symptom-checkers. Out of eight symptom-checkers—K Health, Babylon, ADA, Buoy, Kahun, Mediktor, Symptomae, and Your.MD—Kahun demonstrated the best overall performance in finding the most pertinent insights in a simulated patient conversation.
Kahun’s engine performs clinical reasoning at scale by basing its decisions on the company’s proprietary map of more than 30 million evidence-based medical insights. The tool has the potential to ease the burden on healthcare systems by digitally mimicking the medical-interview process, making it more efficient and accurate, while saving precious time for trained personnel.
IJMI’s study aims to ensure the useful and safe integration of AI-based tools in healthcare. Even though such tools have great potential to assist with healthcare challenges, there has yet to be a mass adoption because physicians can’t blindly trust an algorithm. Kahun’s clinical reasoning solution can be understood and trusted because its insights are referenced and backed by links to originating medical knowledge. Its algorithmic engine utilizes Kahun’s knowledge graph in real time to generate clinical insights tailored to each specific patient.
“We are extremely proud of the IJMI study results,” says Eitan Ron, Co-Founder and CEO of Kahun. “However, we don’t look at ourselves as a symptom checker tasked with merely guessing the right diagnosis. Our explainable AI is designed to conduct clinical reasoning in the same manner a physician would. This study demonstrates that this approach is superior to others already on the market, and I’m convinced that as our model improves, its usefulness for physicians will only become more apparent.”
