Applications of AI in medicine: Intelligent Health AI 2020’s Impressions
By: Anna Choma
Before 2020 every September the world’s brightest AI health brains from pharmaceutical, biotech, medtech, health provisions, clinicians, tech companies, startups, investment and science swarmed to Basel, Switzerland to participate in the only large-scale, global summit focused purely on applications of AI in medicine – Intelligent Health AI. Because of the pandemics, this year the event took place entirely online on the hopin platform with attendees from 164 countries, participation of 3k+ world leaders, 200 speakers, 4 cyberspace innovators & startups presenting, with the involvement of 48 investors, and coverage by 38 international media and TV.
Intelligent Health AI – intelligent event
To say the least the event was huge and packed with dynamic and interactive content: heated discussions, tech demos and interactive workshops streamed from all corners of the world leaving little time to catch breath between sessions. The organizers went to great lengths to provide space for the participants to connect the AI and medicine communities, for example with the networking roulette function where one could introduce oneself to other online delegates in 3 minute speed-dating style sessions and also video and text chat functionalities plus interactive workshop sessions to leverage the outstanding community connections and take away some tangible ROI from the world’s leading AI in medicine event. In the Cyberspace area one could find virtual booths hosted by Intelligent Health AI sponsors and partners to gather information and interact with the hosts.
We are looking forward to next year’s edition in September 2021.
Intelligent Health AI 2020 – themes
Key themes for Intelligent Health AI 2020 included:
Applications of AI in medicine: our impressions
This year our team was represented by our Machine Learning software engineers Bartosz Machura, Krzysztof Kotowski and Wojciech Malara and here are some of their impressions.
Bartosz Machura – unexpected applications of AI in medicine
Day 1: Brilliant conference! Many interesting topics were covered that showed me some unexpected applications of AI in medicine, such as: virtual staining of histopathological samples, attempts of COVID-19 detection from cough or real-time analysis of agents released from an incision during the surgery. Especially worth mentioning were three speeches:
Krzysztof Kotowski – Artificial General Intelligence may be the next game changer
After two days of this conference, I am even more excited to be the part of this community. COVID-19 pandemic forced us to meet online, but also emphasized the real need for robust AI supporting healthcare systems in these hard times. The number of innovative AI applications in Healthcare surprised me – from automated medical documentation, through medical image segmentation, to autonomous surgical robots. The problem is that still only few make a real impact on the global healthcare system. Artificial General Intelligence (great talk from Gary Marcus) may be the next game changer.
One of the strong aspects of the conference was the focus on regulatory requirements of AI solutions (Andrea Biasiucci from Confinis – thanks for the insights into MDCG Guidance 2020-1 on Clinical Evaluation of Medical Device Software). The options of visiting virtual booths and networking gave many opportunities to meet experts from all over the world.
Wojciech Malara – limitations of pure deep learning approach
The talk that was particularly interesting to me was “COVID-19 should be a wake-up call for AI” by Gary Marcus, which was in fact based on an article “The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence“. In the talk, prof. Marcus highlighted some limitations of pure deep learning approach, which is great for learning complex statistical correlations and perceptual classification but performs poorly in terms of reasoning and language understanding. In general, deep learning cannot extrapolate beyond training data. As a step towards more robust AI the author proposed “a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models”.
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See the previous post by Anna Choma: Artificial Intelligence in Radiology: highlights of ECR 2020