Daniel Kvak: So that self-diagnosis is not a road to hell...

24 Mar

Daniel Kvak: So that self-diagnosis is not a road to hell...

With the public availability of tools like Midjourney or ChatGPT, the topic and ideas about the possibilities of using artificial intelligence are beginning to permeate the general public. Meanwhile, those who fell for the fascination with smart algorithms a little earlier are implementing projects that a few years ago were only portrayed in science fiction stories. The autonomous recovery modules familiar from films such as Alien or Elysium may only really be imaginable on screen, but in diagnostics, for example, AI has experienced a steep rise in the last few years. As it turns out, it can save time and money, which can play a significant role in the face of challenges related to an aging population and a shortage of medical personnel. One of those who have joined the digital health race is Mgr. Daniel Kvak. A student of the Faculty of Arts at Masaryk University, where he is working on the use of machine learning in audio-visual, but also the driving force behind the company Carebot, under whose name he is breaking into medical practices. The rise of the ambitious start-up is as precipitous as the cadence with which the young entrepreneur spouts medical concepts indistinguishable from those of a medical professional, and what began during the COVIDU-19 pandemic with a system for classifying lung X-rays is now scaling to other disciplines. One of the projects that Carebot's growing team is working on is a tool for recognizing skin melanomas, especially in patients with darker skin tones. "I was surprised that when Google released the DermAssist app, they didn't take much account of skin tone, when I think it's the most important factor," Kvak observes. After all, the need to learn from mistakes and be as practical as possible is emphasised several times in the interview. That his efforts make sense is confirmed, among other things, by his recent award at the MICAD conference, which focused specifically on computer systems that help with the interpretation of medical images.

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