fd527f44fb451c22f57c06a2d90b1ef6 303856.pdf d3b564ae1af55672394cb6f589b3ca5819e812ad 303856.pdf 868d43b2309fe1cbc97f32ccde8fece205d2654f0d90b81d00141cd9e9df1052 303856.pdf Title: Artificial intelligence: a new field of knowledge for nephrologists? Subject: DOI: 10.1093/ckj/sfad182 Clinical Kidney Journal, 16, 12, 29-07-2023. Abstract: Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information. Keywords: artificial intelligence, kidney, machine learning, natural language processing, nephrology Author: Fayos De Arizón Leonor, Viera Elizabeth R., Pilco Melissa, Perera Alexandre, De Maeztu Gabriel, Nicolau Anna, Furlano Monica, Torra Roser Creator: OUP Producer: Acrobat Distiller 10.0.0 (Windows) CreationDate: Mon Nov 13 06:36:34 2023 CET ModDate: Mon Nov 13 06:37:09 2023 CET Custom Metadata: yes Metadata Stream: yes Tagged: yes UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 612.284 x 790.866 pts Page rot: 0 File size: 1291061 bytes Optimized: yes PDF version: 1.3 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- DMAGFG+CaeciliaLTStd-Heavy Type 1C WinAnsi yes yes no 1270 0 DMAFDL+CaeciliaLTStd-Bold Type 1C Custom yes yes yes 1273 0 DMAFEM+MTSY Type 1C Custom yes yes yes 1278 0 DMAFDI+CaeciliaLTStd-Italic Type 1C WinAnsi yes yes no 1283 0 DMAFDK+CaeciliaLTStd-Roman Type 1C Custom yes yes yes 1286 0 DMAFDJ+dummy-space Type 1C WinAnsi yes yes no 1291 0 DMBLKL+RMTMI Type 1C Custom yes yes yes 195 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-12-05 02:53:29 CET RepresentationInformation: 303856.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-12-04 13:46:49 CET Size: 1291061 Format: PDF Version: 1.3 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf Profile: Linearized PDF, Tagged PDF PDFMetadata: Objects: 1356 FreeObjects: 1 IncrementalUpdates: 1 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: false CenterWindow: false DisplayDocTitle: true NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: OneColumn PageMode: UseOutlines Language: English Outlines: Item: Title: INTRODUCTION Destination: section*.2 Item: Title: MACHINE LEARNING Destination: section*.4 Item: Title: NATURAL LANGUAGE PROCESSING Destination: section*.6 Item: Title: USE OF AI FOR KIDNEY DISEASES Destination: section*.8 Item: Title: Diagnosis Destination: section*.10 Item: Title: Prediction of outcomes Destination: section*.12 Item: Title: Treatment aid Destination: section*.14 Item: Title: CHALLENGES OF AI Destination: section*.16 Item: Title: Data access and quality Destination: section*.18 Item: Title: Data privacy and security Destination: section*.20 Item: Title: Bias Destination: section*.22 Item: Title: Trustworthiness Destination: section*.24 Item: Title: Computing power Destination: section*.26 Item: Title: AI integration Destination: section*.28 Item: Title: AI specialists Destination: section*.30 Item: Title: Legal issues Destination: section*.32 Item: Title: EUROPEAN UNION FRAMEWORK Destination: section*.34 Item: Title: CONCLUSIONS Destination: section*.36 Item: Title: FUNDING Destination: section*.38 Item: Title: CONFLICT OF INTEREST STATEMENT Destination: section*.40 Item: Title: DATA AVAILABILITY STATEMENT Destination: section*.42 Item: Title: REFERENCES Destination: section*.44 Info: Title: Artificial intelligence: a new field of knowledge for nephrologists? Author: Fayos De Arizón Leonor, Viera Elizabeth R., Pilco Melissa, Perera Alexandre, De Maeztu Gabriel, Nicolau Anna, Furlano Monica, Torra Roser Subject: DOI: 10.1093/ckj/sfad182 Clinical Kidney Journal, 16, 12, 29-07-2023. Abstract: Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information. Keywords: artificial intelligence, kidney, machine learning, natural language processing, nephrology Creator: OUP Producer: Acrobat Distiller 10.0.0 (Windows) CreationDate: Mon Nov 13 07:06:34 CET 2023 ModDate: Mon Nov 13 07:07:09 CET 2023 ID: 0x7db23429438e0235e95a8a5ab9a7a6e1, 0x65d909df1a9d94e809b379b6202f4f0c Filters: FilterPipeline: FlateDecode FilterPipeline: DCTDecode Images: Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 143 ImageHeight: 151 ColorSpace: CMYK BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 1339 ImageHeight: 1231 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 96 ImageHeight: 81 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 94 ImageHeight: 80 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 102 ImageHeight: 95 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 151 ImageHeight: 152 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Fonts: Type1: Font: BaseFont: DMAFDI+CaeciliaLTStd-Italic FontSubset: true FirstChar: 45 LastChar: 121 FontDescriptor: FontName: DMAFDI+CaeciliaLTStd-Italic Flags: Serif, Nonsymbolic, Italic FontBBox: -166, -270, 1053, 915 FontFile3: true Encoding: WinAnsiEncoding Font: BaseFont: DMBLKL+RMTMI FontSubset: true FirstChar: 60 LastChar: 62 FontDescriptor: FontName: DMBLKL+RMTMI Flags: Symbolic, Italic FontBBox: 0, -213, 987, 680 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: DMAGFG+CaeciliaLTStd-Heavy FontSubset: true FirstChar: 48 LastChar: 89 FontDescriptor: FontName: DMAGFG+CaeciliaLTStd-Heavy Flags: Nonsymbolic, ForceBold FontBBox: -170, -277, 1146, 970 FontFile3: true Encoding: WinAnsiEncoding Font: BaseFont: DMAFDK+CaeciliaLTStd-Roman FontSubset: true FirstChar: 2 LastChar: 243 FontDescriptor: FontName: DMAFDK+CaeciliaLTStd-Roman Flags: Serif, Symbolic FontBBox: -167, -270, 1136, 926 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: DMAFDL+CaeciliaLTStd-Bold FontSubset: true FirstChar: 2 LastChar: 121 FontDescriptor: FontName: DMAFDL+CaeciliaLTStd-Bold Flags: Serif, Symbolic, ForceBold FontBBox: -166, -273, 1141, 938 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: DMAFDJ+dummy-space FontSubset: true FirstChar: 32 LastChar: 32 FontDescriptor: FontName: DMAFDJ+dummy-space Flags: Nonsymbolic FontBBox: 0, 0, 0, 0 FontFile3: true Encoding: WinAnsiEncoding Font: BaseFont: DMAFEM+MTSY FontSubset: true FirstChar: 2 LastChar: 177 FontDescriptor: FontName: DMAFEM+MTSY Flags: Symbolic FontBBox: 0, -954, 1043, 796 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true XMP: 2023-11-13T11:06:34+05:30 2023-11-13T11:07:09+05:30 2023-11-13T11:07:09+05:30 OUP Acrobat Distiller 10.0.0 (Windows) artificial intelligence, kidney, machine learning, natural language processing, nephrology VoR 10.1093/ckj/sfad182 2048-8513 Clinical Kidney Journal journal 16 10.1093/ckj/sfad182 https://doi.org/10.1093/ckj/sfad182 © The Author(s) 2023. Published by Oxford University Press on behalf of the ERA. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com All Rights Reserved application/pdf doi:10.1093/ckj/sfad182 © The Author(s) 2023. Published by Oxford University Press on behalf of the ERA. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Fayos De Arizón Leonor Viera Elizabeth R. Pilco Melissa Perera Alexandre De Maeztu Gabriel Nicolau Anna Furlano Monica Torra Roser Oxford University Press DOI: 10.1093/ckj/sfad182 Clinical Kidney Journal, 16, 12, 29-07-2023. Abstract: Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information. artificial intelligence kidney machine learning natural language processing nephrology Artificial intelligence: a new field of knowledge for nephrologists? 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