Hanzhou Li , John T. Moon , Vishal Shankar , Janice Newsome , Judy Gichoya , Zachary Bercu
{"title":"Health inequities, bias, and artificial intelligence","authors":"Hanzhou Li , John T. Moon , Vishal Shankar , Janice Newsome , Judy Gichoya , Zachary Bercu","doi":"10.1016/j.tvir.2024.100990","DOIUrl":null,"url":null,"abstract":"<div><div>Musculoskeletal (MSK) pain leads to significant healthcare utilization, decreased productivity, and disability globally. Due to its complex etiology, MSK pain is often chronic and challenging to manage effectively. Disparities in pain management—influenced by provider implicit biases and patient race, gender, age, and socioeconomic status—contribute to inconsistent outcomes. Interventional radiology (IR) provides innovative solutions for MSK pain through minimally invasive procedures, which can alleviate symptoms and reduce reliance on opioids. However, IR services may be underutilized, especially due to current treatment paradigms, referral patterns, and in areas with limited access to care. Artificial intelligence (AI) presents a promising avenue to address these inequities by analyzing large datasets to identify disparities in pain management, recognizing implicit biases, improving cultural competence, and enhancing pain assessment through multimodal data analysis. Additionally, patients who may benefit from an IR pain procedure for their MSK pain may then receive more information through their providers after being identified as a candidate by AI sifting through the electronic medical record. By leveraging AI, healthcare providers can potentially mitigate their biases while ensuring more equitable pain management and better overall outcomes for patients.</div></div>","PeriodicalId":51613,"journal":{"name":"Techniques in Vascular and Interventional Radiology","volume":"27 3","pages":"Article 100990"},"PeriodicalIF":1.4000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Techniques in Vascular and Interventional Radiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1089251624000465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Musculoskeletal (MSK) pain leads to significant healthcare utilization, decreased productivity, and disability globally. Due to its complex etiology, MSK pain is often chronic and challenging to manage effectively. Disparities in pain management—influenced by provider implicit biases and patient race, gender, age, and socioeconomic status—contribute to inconsistent outcomes. Interventional radiology (IR) provides innovative solutions for MSK pain through minimally invasive procedures, which can alleviate symptoms and reduce reliance on opioids. However, IR services may be underutilized, especially due to current treatment paradigms, referral patterns, and in areas with limited access to care. Artificial intelligence (AI) presents a promising avenue to address these inequities by analyzing large datasets to identify disparities in pain management, recognizing implicit biases, improving cultural competence, and enhancing pain assessment through multimodal data analysis. Additionally, patients who may benefit from an IR pain procedure for their MSK pain may then receive more information through their providers after being identified as a candidate by AI sifting through the electronic medical record. By leveraging AI, healthcare providers can potentially mitigate their biases while ensuring more equitable pain management and better overall outcomes for patients.
期刊介绍:
Interventional radiology is an area of clinical diagnosis and management that is highly technique-oriented. Therefore, the format of this quarterly journal, which combines the visual impact of an atlas with the currency of a journal, lends itself perfectly to presenting the topics. Each issue is guest edited by a leader in the field and is focused on a single clinical technique or problem. The presentation is enhanced by superb illustrations and descriptive narrative outlining the steps of a particular procedure. Interventional radiologists, neuroradiologists, vascular surgeons and neurosurgeons will find this a useful addition to the clinical literature.