Bridging the gap between pathologist and algorithm
Posted 16th October 2019 by Liv Sewell
Could AI replace pathologists? As we look forward to the 6th Digital Pathology and AI Congress: Europe on the 5th – 6th December 2019, we look back at Dr Hamid Tizhoosh’s keynote presentation from last year.
Hamid Tizhoosh is director of the Knowledge Inference in Medical Image Analysis (KIMIA) Lab in the Engineering Faculty at the University of Waterloo. He is also a member of the Waterloo AI Institute, and a faculty affiliate to the Vector Institute. As part of his commercial activities, he is presently the AI advisor of Huron Digital Pathology, St Jacobs, Canada. In the keynote presentation ‘Learning to search in large image archives: how AI agents can serve pathologists’, Dr Tizhoosh explained how Digital pathology can gain huge benefits from rapid image search and the effective extraction of knowledge from large medical archives via artificial intelligence (AI). He also commented on the limitations of AI.
Tizhoosh outlined how AI uses software algorithms to represent data, classify data, and search for similar instances, either in a supervised way (with the pathologist’s direct and indirect instructions) or in an unsupervised approach. And how this facilitates identification of anatomical and pathological similarities, significantly enhances the clinical workflow, and ultimately paves the way for more informed diagnosis and better patient outcome. Importantly, effective archive searching is the bridge between computer algorithms and pathologists, adding a new dimension to clinical practice in an ever-more demanding working environment.
‘Learning is just distance minimisation between what AI is calculating and what pathologists have put forward as labelled or annotated data,’ he explained. The deep learning aspect of AI has proved most effective in this respect with the ability to classify images (such as deciding whether an image is cancerous or not, or adding grading); segment by focusing on a specific part of the image; or search the archive to find similar cases. ‘Image search is the unsupervised part,’ he said, ‘because you do not need a pathologist to teach the software about what to do.’
Tizhoosh also addressed the question of whether AI can, given its capabilities, ultimately replace the pathologist. ‘The answer to that is no,’ Tizhoosh confirmed. ‘AI is not at the stage where it can write a pathology report but you can use AI to search and find similar cases so that the pathologist can write a better report. The human operator is the ultimate validation of AI, so that means there will be no full automation and the human will always be in the loop from the beginning – either to teach, validate, accept or reject. ‘You cannot replace the pathologist because AI is still very weak and it is the pathologist who writes the detailed report, whereas AI just gives a simple answer. Algorithms cannot provide explanations such as why it came to a decision, or why do you think the image depicts cancerous regions. AI application is just to assist. So, asking if AI can replace the pathologist is not a serious question.’
To truly have AI that can perform at this level will require it to be capable of multi-tasking and to have human level intelligence such as ethical cognition, he pointed out. However, the benefits of AI are clearly very significant given its ability to rapidly search out similar cases with annotated information. A big advantage of this is the element of ‘virtual peer review’ because many of the reports in the archive will be from different pathologists. ‘AI can access the knowledge of colleagues even when they are not there, but you need a large archive of images for that, let’s say, upwards of half a million,’ Tizhoosh said. For an image search in such large archives to be effective, bar-coding (binary tagging) is the key step. ‘You can search half a million scans a second,’ he continued. ‘What you can’t do is bring up 500 glass slides from the basement and search through them – this is where we really start to think about the advantages of digital pathology and if you have a large enough archive the search can really contribute to consensus.’
This article was written by Mark Nicholls and was originally published in Healthcare-in-Europe on 25.04.2019. It is republished here with their kind permission.
Deep learning and artificial intelligence are revolutionising pathology practice and improving patient outcomes. Explore the latest developments alongside other practitioners, providers and regulators at the 6th Digital Pathology and AI Congress: Europe this December. Discover the programme.
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