Diagnostics
That is, the AI systems provide assistance to improve safety and efficiency, but do not take over from the pilot or human expert completely.” That is not to say, however, that this could not change in the future. Like Kartasalo, Guermazi talks about these technologies in the here and now – “today they should remain assistive” – but he is aware that tomorrow might tell a different story: “as the field progresses,” he says, “it is obvious that for some specific tasks, AI will outperform humans. However, there is a liability issue that needs to be solved if AI becomes autonomous.”
Ozcan and his team’s deep learning framework transforms reflectance confocal microscopy images into a user- friendly format.
Ali Guermazi, MD, PhD, chief of radiology at VA Boston healthcare system and professor of radiology and medicine at Boston University School of Medicine, has been working on BoneView, an algorithm developed by medtech start-up GLEAMER to detect fractures on X-rays. Like Kartasalo’s and Ozcan’s studies, BoneView has also been “trained”, this time on accurately annotated X-ray images of the limbs, pelvis, rib cage and thoracolumbar spine. “These X-rays,” Guermazi explains, “were collected from multiple institutions and acquired on a large variety of systems. As an output, BoneView provides a summary table and replicated images with bounding boxes around regions of interest.”
What is it that this algorithm can pick up that an ordinary X-ray cannot? “Missed fractures on conventional radiographs are one of the most common causes of medical errors in the emergency department,” Guermazi explains, “and can lead to potentially serious complications, delays in diagnostic and therapeutic management, and the risk of legal claims by patients. As we showed in the study, AI can detect fractures with a much higher sensitivity than human readers alone, as well as improve their specificity, for example, by reducing their false positive rate.”
AI’s proximity to human expertise For all the talk of learning and training, both Guermazi and Kartasalo are adamant that these technologies remain assistive – at least for now. “Currently,” Kartasalo says, “we see the relationship between the human expert and the machine as comparable to the situation with modern cars and aircraft, for example.
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Just how we solve the liability issue remains to be seen. As Kartasalo notes, “this question needs to be ultimately addressed in line with the local jurisdiction and regulatory legislation, such as the in vitro diagnostic regulation (IVDR) in the EU. The questions of responsibility and patient safety are naturally key ones that need to be answered in view of widespread clinical adoption of AI-based diagnostics.” For now, there are still challenges facing the adoption of these technologies at a more basic level, such as: standardising them globally, across different geographies; the requirement for integrated digitised systems; the cost of equipment; and the question of return on investment. “In the current healthcare cost pressure environment,” Guermazi says, “there probably needs to be further evaluation on health economic benefits.”
At this moment in time, AI still remains an art, a skill, the cunning of the human hand, to recall that etymological root term techne. As long as these sophisticated tools remain assistive – shouldering the burden of more monotonous tasks, helping clinicians to achieve more accurate diagnoses and spotting signs that might otherwise go unnoticed – then there is no question these technologies have the potential to transform medical diagnosis for the better. But, as scientists focus on the details, and as hospitals ask legitimate questions around cost and efficacy, we should be careful not to lose sight of the learning of which these machines are capable. As Kartasalo notes, “AI algorithms are very efficient in capturing whichever patterns are present in their training data. If the data is biased, in the sense that it enforces social biases or prejudice, there is a very high chance that an AI algorithm naively trained on such data will capture these biases and replicate them in its decision-making.” The future of medicine undoubtedly contains AI, but care has to be taken at every stage, from data collection, to the rigorous validation of algorithms, to best practice in the hospital setting. This means calibrating the relationship between human and machine. Technology has long been a friend to modern medicine, and AI is just one more solution to the problem of human error. But strip out all margin of human error, and we risk removing human judgement, care and accountability too.
Practical Patient Care /
www.practical-patient-care.com
Light: Science & Applications: 10, 233 (2021)
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