{"id":23182,"date":"2023-04-14T07:38:21","date_gmt":"2023-04-14T11:38:21","guid":{"rendered":"https:\/\/orionscoldfire.com\/?p=23182"},"modified":"2023-04-14T07:40:50","modified_gmt":"2023-04-14T11:40:50","slug":"guest-contributor-arthurincali-14apr2023-scientific-dark-ages","status":"publish","type":"post","link":"https:\/\/orionscoldfire.com\/index.php\/2023\/04\/14\/guest-contributor-arthurincali-14apr2023-scientific-dark-ages\/","title":{"rendered":"Guest Contributor – ArthurinCali \u2013 14APR2023 – Scientific Dark Ages"},"content":{"rendered":"

AI medical imaging potential should not be stifled.<\/em><\/a><\/p>\n

\u00a0<\/em><\/p>\n

“Nothing in life is to be feared, it is only to be understood.<\/strong>\u00a0Now is the time to understand more, so that we may fear less.”<\/strong><\/em><\/p>\n

-Marie Curie (1867-1934)<\/em><\/p>\n

 <\/p>\n

The de facto position concerning new medical discoveries cannot be that it will inevitably be used for sinister purposes. This anti-science viewpoint stifles innovation and progress for humanity, thus stunting potential breakthroughs in medicine. Tools available to researchers and health professionals are just that, tools. The usage and application are dependent on who wields these instruments. It is not hard to imagine the first Neanderthals who discovered fire might have been dubious about its utilization, once one of them tried to touch it.<\/p>\n

Recently it was found that Artificial Intelligence<\/a>\u00a0(AI) used to screen medical images can detect with near-perfect accuracy the patient\u2019s racial ethnicity without prior knowledge from outside inputs. This ability for AI covered a broad series of image types; from CT scans to chest X-Rays, and persisted over all anatomical regions. This finding was replicated even from images that were cropped, corrupted, or not ideally clear. Even more surprising is that human experts trained to analyze these scans cannot determine the patient\u2019s race, yet AI trained with deep learning developed this ability by itself.<\/p>\n

The AI utilizes\u00a0\u2018Deep Learning\u2019<\/strong><\/a>\u00a0as the machine learning model that imitates the human learning process to a degree with predictive analytics, and complex learning algorithms.<\/p>\n

A good analogy describes the process as:<\/p>\n

\u201cTo understand deep learning, imagine a toddler whose first word is\u00a0dog<\/em>. The toddler learns what a dog is — and is not — by pointing to objects and saying the word\u00a0dog<\/em>. The parent says, “Yes, that is a dog,” or, “No, that is not a dog.” As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler does, without knowing it, is clarify a complex abstraction — the concept of dog — by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.\u201d<\/p>\n

In this fashion, deep learning differs from traditional linear algorithms that are dictated by the computer programmer. A deep learning model will independently sift through massive data sets to create complex statistical models from large amounts of unstructured data. This represents a new frontier in AI technology. Prior to the 21st century, access to big data sets and cloud computing were out of reach for many computer programmers.<\/p>\n

Not everyone in the medical field sees the potential in AI recognition.\u00a0This article from MIT<\/a>\u00a0discounts any benefit to the computer model\u2019s ability to detect race.<\/p>\n

\u201cThe fact that algorithms ‘see’ race, as the authors convincingly document, can be dangerous. But an important and related fact is that, when used carefully, algorithms can also work to counter bias,\u201d says Ziad Obermeyer, associate professor at the University of California at Berkeley, whose research focuses on AI applied to health.<\/p>\n

This understandable caution for bias by AI is the prevailing theme for a significant part of the medical community. Numerous\u00a0papers<\/a>\u00a0and\u00a0articles<\/a>\u00a0sound the\u00a0alarm<\/a>\u00a0for potential\u00a0abuses<\/a>\u00a0of AI racial recognition. However, not one study or opinion piece expanded on the potential uses for AI\u2019s talent to discern race in medical imaging. Remember, no human experts can do this, regardless of experience or training. What if this could unlock new, highly effective therapies tailored to different races depending on the disease? Maybe this leads to discovering distinct correlations that would allow for earlier detections of cancer and other diseases. AI using multivariate analysis would give all patients an edge in their treatment plans. An entirely new realm of possibilities could open up for science, but only if the courage is there to look for it.<\/p>\n

This may be of no value in the field of medicine, and possibly even be a feature of AI that needs to be eliminated entirely. Yet to dismiss out of hand the ability for AI to detect racial identity in medical imaging without more research into potential uses would be folly and a discredit to the goal of furthering medical science and artificial intelligence.<\/p>\n

Links:<\/p>\n

AI recognition of patient race in medical imaging: a modelling study<\/strong><\/p>\n

https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00063-2\/fulltext<\/a><\/p>\n

Artificial intelligence predicts patients\u2019 race from their medical images<\/strong><\/p>\n

https:\/\/news.mit.edu\/2022\/artificial-intelligence-predicts-patients-race-from-medical-images-0520<\/a><\/p>\n

AI systems can detect patient race, creating new opportunities to perpetuate health disparities<\/strong><\/p>\n

https:\/\/news.emory.edu\/stories\/2022\/05\/hs_ai_systems_detect_patient_race_27-05-2022\/story.html<\/a><\/p>\n

Risks of AI Race Detection in the Medical System<\/strong><\/p>\n

https:\/\/hai.stanford.edu\/policy-brief-risks-ai-race-detection-medical-system<\/strong><\/a><\/p>\n

AI Can Detect Race When Clinicians Cannot, Increasing Risk of Bias<\/strong><\/p>\n

https:\/\/healthitanalytics.com\/news\/ai-can-detect-race-when-clinicians-cannot-increasing-risk-of-bias<\/a><\/p>\n

AI programs can tell race from X-rays, but scientists don\u2019t know how. Here\u2019s why that\u2019s bad.<\/strong><\/p>\n

https:\/\/www.boston.com\/news\/health\/2022\/05\/18\/scientists-create-ai-race-from-x-rays-dont-know-how-it-works-harvard-mit\/<\/a><\/p>\n

Hidden in Plain Sight: If AI Can Detect Race, What About Bias?<\/strong><\/p>\n

https:\/\/www.medscape.com\/viewarticle\/977619<\/a><\/p>\n

Deep Learning<\/strong><\/p>\n

https:\/\/www.techtarget.com\/searchenterpriseai\/definition\/deep-learning-deep-neural-network<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

AI medical imaging potential should not be stifled. \u00a0 “Nothing in life is to be feared, it is only to be understood.\u00a0Now is the time<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[3845,5,1489,3545],"tags":[3773,4011,4010,165],"class_list":["post-23182","post","type-post","status-publish","format-standard","hentry","category-arthurincali","category-current-events","category-guest-contributors","category-science","tag-artificial-intelligence","tag-deep-learning","tag-machine-learning","tag-science"],"yoast_head":"\nGuest Contributor - ArthurinCali \u2013 14APR2023 - Scientific Dark Ages - Orion's Cold Fire<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/orionscoldfire.com\/index.php\/2023\/04\/14\/guest-contributor-arthurincali-14apr2023-scientific-dark-ages\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Guest Contributor - 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