Fairness of artificial intelligence in healthcare: review and recommendations

被引：103

作者：

Ueda, Daiju ^{[1
]}

Kakinuma, Taichi ^{[2
]}

Fujita, Shohei ^{[3
]}

Kamagata, Koji ^{[4
]}

Fushimi, Yasutaka ^{[5
]}

Ito, Rintaro ^{[6
]}

Matsui, Yusuke ^{[7
]}

Nozaki, Taiki ^{[8
]}

Nakaura, Takeshi ^{[9
]}

Fujima, Noriyuki ^{[10
]}

Tatsugami, Fuminari ^{[11
]}

Yanagawa, Masahiro ^{[12
]}

Hirata, Kenji ^{[13
]}

Yamada, Akira ^{[14
]}

Tsuboyama, Takahiro ^{[12
]}

Kawamura, Mariko ^{[6
]}

Fujioka, Tomoyuki ^{[15
]}

Naganawa, Shinji ^{[6
]}

机构：

[1] Osaka Metropolitan Univ, Grad Sch Med, Dept Diagnost & Intervent Radiol, 1-4-3 Asahi Machi,Abeno Ku, Osaka 5458585, Japan

[2] STORIA Law Off, Chuo Ku, Kobe, Hyogo, Japan

[3] Univ Tokyo, Dept Radiol, Bunkyo Ku, Tokyo, Japan

[4] Juntendo Univ, Dept Radiol, Grad Sch Med, Bunkyo Ku, Tokyo, Japan

[5] Kyoto Univ, Dept Diagnost Imaging & Nucl Med, Grad Sch Med, Sakyo Ku, Kyoto, Japan

[6] Nagoya Univ, Dept Radiol, Grad Sch Med, Nagoya, Aichi, Japan

[7] Okayama Univ, Fac Med Dent & Pharmaceut Sci, Dept Radiol, Kita Ku, Okayama, Japan

[8] Keio Univ, Dept Radiol, Sch Med, Shinjuku Ku, Tokyo, Japan

[9] Kumamoto Univ, Dept Diagnost Radiol, Grad Sch Med, Chuo Ku, Kumamoto, Japan

[10] Hokkaido Univ Hosp, Dept Diagnost & Intervent Radiol, Sapporo, Japan

[11] Hiroshima Univ, Dept Diagnost Radiol, Minami Ku, Hiroshima, Japan

[12] Osaka Univ, Dept Radiol, Grad Sch Med, Suita, Osaka, Japan

[13] Hokkaido Univ, Grad Sch Med, Dept Diagnost Imaging, Kita Ku, Sapporo, Hokkaido, Japan

[14] Shinshu Univ, Dept Radiol, Sch Med, Matsumoto, Nagano, Japan

[15] Tokyo Med & Dent Univ, Dept Diagnost Radiol, Bunkyo Ku, Tokyo, Japan

来源：

JAPANESE JOURNAL OF RADIOLOGY | 2024年 / 42卷 / 01期

关键词：

Fairness; Bias; Artificial intelligence; Healthcare; Medicine; Review; BIG DATA; CHEST RADIOGRAPHS; LEARNING-MODELS; VALIDATION; BIAS; PERFORMANCE; DISEASE; PRIVACY; ETHICS; ISSUES;

D O I：

10.1007/s11604-023-01474-3

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

引用

页码：3 / 15

页数：13

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