AI in radiology: Where are we today?
Dr Mahajan began the panel discussion on ‘Artificial intelligence in radiology: Where are we today?’ by asking the panelists about the differences between radiomics, deep learning, machine learning and AI. He also briefed the audience about image acquisition and image post processing in radiology.
While putting forth his views on radiomics, Dr Ganeshan elaborated on its features and quantifiable characteristics. He mentioned that radiomix has more handcrafted features and patterns on the images. Radiomics tries to emulate what a radiologist does but provides objectivity to it, independent of your background, experience, and education. The idea is to bring objectiveness, repeatability, reproducibility and so on.
According to Dr Ganeshan, AI has a black box approach where it is able to provide an answer but not able to establish from where the success came in. He also mentioned about the pros and cons to all these methodologies.
The panellists deliberated on deep learning, different aspects of image processing and quantification. They all agreed to the fact that radiomics and AI, when combined, can be a win-win situation in the radiology sector.
Sinha gave an update on how Columbia Asia Hospitals are implementing Qure.ai algorithm to interpret radiology images. She informed that work on the specific algorithm started in 2016, and concluded in 2017. The hospital has worked on how the algorithm can on nine different abnormalities using the technique and the results were satisfying. According to her, the hospital is already in the process of launching this technique where AI will help simplify the work process.
Sinha further mentioned that the algorithm is touted to become an excellent audit tool for X rays and has performed phenomenally well.
Gune mentioned that AI will be an add-on advantage for radiologists. A tool without human intervention is the future of radiology where radiologists will be able to prioritise three-four X-rays and the remaining X-rays can be looked into later.
He cited the example wherein around the globe two billion chest X-rays are developed per annum and elaborated how AI will play an important role in streamlining its reading.
Dr Kharat spoke on ways to deal with the ground realities and mentioned that when good data is available to train algorithms, the sector will surge ahead.
He urged start-up companies to invest immensely to help allocate data. He mentioned that this is the year of innovation for the sector. He also said that there is a need to look for a long-term perspective and may be in a decade or so once the product matures, it can be used in practice.
Dr Vasanth Venugopal elucidated on the need to curate AI algorithms, controll test settings, on payment modules and how companies can sustain themselves. The panellists agreed to that data needs to be anonymised to help the radiology sector become much more smarter and agile. They also recommended that one should also look for solutions to archive images intelligently.
- Even though there is a lot of hype around AI, the radiology sector is at crossroads where it is concerned. The applications of AI are evolving but the fundamental aspects of AI may have reached a dead end. However, there is still hope.
- Radiomics is high throughput extraction of quantitative imaging features or texture from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. It tries to emulate what a radiologist does, and puts those requirements in everyday practice.
- In future, AI is going to learn from radiologists. It will be radiologists who will be the biggest data support for AI feeds.
- It is important to note that the concept of one size fits all, cannot be applied in radiology and AI, radiomics etc.
- There is a need for laws related to ownership of patient/medical data. This is crucial to protect the increasing misuse of medical data.