Raw LLM Responses

Dear Dr. Cellini, Naglis, Chief Medical Officer and researcher from Oxipit (also a radiologist) here, found your excellent channel, an honor to be featured! First of all, thank you for an excellent video and your valid and reasonable points. Let me provide some feedback: 1) Software made no (clinically significant) errors during the pilot phase. Your comment was correct that it does not list what was considered a non clinically significant error. Let me elaborate briefly about this: clinically significant errors are defined as errors which if unreported have the potential to cause patient harm. This would include everything that would be broadly considered clinically significant and/or actionable (nodules, pneumothoraces, consolidations, subtle minor nodular opacities, which cannot be excluded to be significant and many others, which are too many to be listed). Some findings such as degenerative changes of the thoracic spine, some age correlated sclerotic changes of the aortic arch and other similar ones, can be considered clinically insignificant and might be classified among the normal studies. There will be findings, which might depend on the institution if they are to be considered as significant or not, and this is identified during the initial retrospective and prospective validation stage in each institution. Any steps towards autonomy are only made once the radiologists from a specific institution are confident in the validation and safety of the solution. 2) Who is liable for the AI software? To answer this question shortly, once the software is running in an autonomous mode we aim to take the responsibility for the analysis of the studies, which were classified as normal by the software. You have correctly noted, that if that is not the case, there is marginal value to be gained for the radiologist, as at most this will save only a few seconds of time per study. Before we get to the autonomous stage and the final signature is still on the institution/radiologists side, the liability is the same as it is now - on the institution side. Having said that, there are questions which we cannot yet fully answer, we aim to be transparent about that. Even though we are already certified, there are national and international laws which are still to be worked out. CE certificate is not the end of this process - it is a benchmark which allows the next steps to be taken. 3) Will this actually help radiologists? How much it will help exactly depends highly on the institution and the market. A few examples: A primary care institution which makes 100 CXR studies per day, and lets say on average 80 of those studies are normal. Ultimately the software could potentially automate the reporting of ~35 of the studies, which could result in tangible resource/cost savings and allow the radiologists to spend more time on more interesting and challenging cases (either CXRs or other modalities). Also, there are institutions in some markets who already cannot report a significant fraction of their studies (both in western and the developing world countries), and some patients do not get a report for their chest X-ray either altogether, or during a useful amount of time. We believe automating even a sample of these studies can significantly contribute to improving patient outcomes. 4) How much does the software actually cost? Even though I cannot give a specific price per study (this depends on the market and the cost per CXR in a specific market) this is projected to cost less than the radiologist resources which would be required to report the automated studies in a regular way, with a significant margin. Finally, in regards to your finishing notes about if the radiologists are at risk to lose a significant amount of work anytime soon: short answer - no. This is the first step to automate a fraction of the normal studies of one radiological modality, volumes of which are already too large for the current radiologist resources to properly manage. It is likely that somewhat soon we will see some similar examples in a couple of other modalities. However, I do not think that it is or will be possible to automate abnormal or borderlilne studies anytime soon, mostly because of factors not related to the AI performance, but because of radiological factors, such as the importance of clinical information/context and interreader subjectivity. For the obviously normal studies the effect of these factors can be minimized, but for abnormals they will remain very substantial. Therefore the large majority of the radiological studies will remain in the radiologist's hands for the foreseeable future, unless we figure out general AI soon, which will take all of our jobs and create an utopia on earth. Thank you once again for the feature and if you would like to discuss further, we are open to your questions :)