Bruno Fionda , Elisa Placidi , Mischa de Ridder , Lidia Strigari , Stefano Patarnello , Kari Tanderup , Jean-Michel Hannoun-Levi , Frank-André Siebert , Luca Boldrini , Maria Antonietta Gambacorta , Marco De Spirito , Evis Sala , Luca Tagliaferri
{"title":"介入放射治疗（近距离放射治疗）中的人工智能：加强以患者为中心的护理，满足患者需求","authors":"Bruno Fionda , Elisa Placidi , Mischa de Ridder , Lidia Strigari , Stefano Patarnello , Kari Tanderup , Jean-Michel Hannoun-Levi , Frank-André Siebert , Luca Boldrini , Maria Antonietta Gambacorta , Marco De Spirito , Evis Sala , Luca Tagliaferri","doi":"10.1016/j.ctro.2024.100865","DOIUrl":null,"url":null,"abstract":"<div><div>This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.</div></div>","PeriodicalId":10342,"journal":{"name":"Clinical and Translational Radiation Oncology","volume":"49 ","pages":"Article 100865"},"PeriodicalIF":2.7000,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs\",\"authors\":\"Bruno Fionda , Elisa Placidi , Mischa de Ridder , Lidia Strigari , Stefano Patarnello , Kari Tanderup , Jean-Michel Hannoun-Levi , Frank-André Siebert , Luca Boldrini , Maria Antonietta Gambacorta , Marco De Spirito , Evis Sala , Luca Tagliaferri\",\"doi\":\"10.1016/j.ctro.2024.100865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.</div></div>\",\"PeriodicalId\":10342,\"journal\":{\"name\":\"Clinical and Translational Radiation Oncology\",\"volume\":\"49 \",\"pages\":\"Article 100865\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical and Translational Radiation Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405630824001423\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical and Translational Radiation Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405630824001423","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs
This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.
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