The Authors Guild and 17 authors recently filed a suit against OpenAI for copyright infringement of their works of fiction on behalf of writers whose works were used to train GPT. The complaint states that “Defendants then fed Plaintiffs’ copyrighted works into their…algorithms designed to output human-seeming text responses” and that “at the heart of these algorithms is systematic theft on a mass scale.” How different is this situation from the developments in medicine where physicians are giving away their knowledge to artificial intelligence (AI) on a voluntary basis and spend hours of valuable research time sharing expert knowledge with AI systems? AI has entered the medical field so rapidly and unobtrusively that it seems as if its interactions with the profession have been accepted without due diligence or in-depth consideration. Fogo, Agnes & Kronbichler, Andreas & Bajema, Ingeborg. (2024). AI's Threat to the Medical Profession. JAMA. 10.1001/jama.2024.0018.
1 Following this enormous effort, the algorithm will do its job in an instant. There is the assumption that algorithms may soon be used to perform the tedious tasks that are regarded as time-consuming and not challenging intellectually, saving precious time. It is conceivable that in the near future, pathologists will not only receive scanned slides of kidney biopsies, but that these will be accompanied by a listing that features data such as the number of glomeruli and area of interstitial fibrosis. With this information readily at hand, the pathologist would only have to focus on the more complex lesions to generate a diagnosis (Figure). The drawback of this situation is that if pathologists are no longer required to evaluate the basic histology elejama.com
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(Reprinted) JAMA Published online January 19, 2024 2024 American Medical Association. All rights reserved. 2024 American Medical Association. All rights reserved. Downloaded from jamanetwork.com by Medizinisch-Biologische Fachbibliothek user on 01/19/2024 E1 Opinion Viewpoint Figure. Whole Slide Image of Kidney Biopsy in Artificial Intelligence (AI) Staining Accompanied by AI-Generated Data
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For the moment, it is possible that the input-output strategy may result in new hypotheses about disease mechanisms, perhaps even allowing for proof-of-concept clinical trials. Indeed, the current era could benefit from new ideas that AI brings becauseitliterallyisthinkingoutoftheboxandprovidesconstructsthat we had never thought of before. But the other side is that if we fail to understandtheprocessesunderlyingthenewlydefinedconstructsgenerated by AI, no logical treatment to change the correlative outcome can be developed. The remaining question is which concept is proven in a proof-of-concept trial based on histological constructs without any meaning. Recent studies making use of unsupervised machine learning have identified tissue areas that have no names in traditional kidney pathology,6,7 but very little effort was made to understand them, questioning whether the new ideas and hypotheses that AI brings will be used for scientific inte
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No. of glomeruli: Global sclerosis: Segmental sclerosis detected: GBM changes detected: Crescents detected: Endocapillary hypercellularity: Podocyte density: Mesangial hypercellularity (maximum): Adhesions: Interstitial fibrosis: Tubular atrophy: Inflammation, area: Inflammatory cells detected: 23 4 No No No 2% 86.8 (below normal value) 5 No 17.4% 13.9% 12.1% Lymphocytes (82%) Granulocytes (1.2%) Plasma cells (3.5%) Undetermined (13.3%) Still in process Still in process IF/IH: EM: The staining symbolizes a new, not currently used stain developed for AI purposes. EM indicates electron microscopy; GBM, glomerular basement membrane; and IF/IH, immunofluorescence/immunohistochemistry. outcome,translatinganotfurtherdefinedinputintoanoutputfocused on clinical decision-making only, without any knowledge of the pathogenic processes in between. It can only be hoped that humans will be abletocatchupwiththenewlydefinedconstructsaslongastheknowl-
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