Bias in medical AI: Implications for clinical decision-making DOI Creative Commons
James M. Cross,

Michael A. Choma,

John A. Onofrey

и другие.

PLOS Digital Health, Год журнала: 2024, Номер 3(11), С. e0000651 - e0000651

Опубликована: Ноя. 7, 2024

Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences, especially applications that involve decision-making. Left unaddressed, biased lead to substandard decisions perpetuation exacerbation of longstanding healthcare disparities. We discuss potential at different stages development pipeline how they affect algorithms Bias occur data features labels, model evaluation, deployment, publication. Insufficient sample sizes for certain patient groups result suboptimal performance, algorithm underestimation, clinically unmeaningful predictions. Missing findings also produce behavior, including capturable but nonrandomly missing data, such as diagnosis codes, is not usually or easily captured, social determinants health. Expertly annotated labels used train supervised learning models may reflect implicit cognitive care practices. Overreliance on performance metrics during obscure bias diminish a model's utility. When applied outside training cohort, deteriorate from previous validation do so differentially across subgroups. How end users interact with deployed solutions introduce bias. Finally, where are developed published, by whom, impacts trajectories priorities future development. Solutions mitigate must be implemented care, which include collection large diverse sets, statistical debiasing methods, thorough emphasis interpretability, standardized reporting transparency requirements. Prior real-world implementation settings, rigorous through trials critical demonstrate unbiased application. Addressing crucial ensuring all patients benefit equitably AI.

Язык: Английский

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications DOI Creative Commons
Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi

и другие.

Journal Of Big Data, Год журнала: 2023, Номер 10(1)

Опубликована: Апрель 14, 2023

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate train frameworks. Usually, manual labeling needed provide labeled data, which typically involves human annotators with vast background knowledge. This annotation process costly, time-consuming, and error-prone. every framework fed by significant automatically learn representations. Ultimately, larger would generate better model its performance also application dependent. issue the main barrier for dismissing use DL. Having sufficient first step toward any successful trustworthy application. paper presents holistic survey on state-of-the-art techniques deal models overcome three challenges including small, imbalanced datasets, lack generalization. starts listing techniques. Next, types architectures are introduced. After that, solutions address listed, such as Transfer Learning (TL), Self-Supervised (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these were followed some related tips about acquisition prior purposes, well recommendations ensuring trustworthiness dataset. The ends list that suffer from scarcity, several alternatives proposed in order more each Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, Cybersecurity. To best authors’ knowledge, this review offers comprehensive overview strategies tackle

Язык: Английский

Процитировано

337
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine DOI Creative Commons
Jakub Kufel,

Katarzyna Bargieł-Łączek,

Szymon Kocot

и другие.

Diagnostics, Год журнала: 2023, Номер 13(15), С. 2582 - 2582

Опубликована: Авг. 3, 2023

Machine learning (ML), artificial neural networks (ANNs), and deep (DL) are all topics that fall under the heading of intelligence (AI) have gained popularity in recent years. ML involves application algorithms to automate decision-making processes using models not been manually programmed but trained on data. ANNs a part aim simulate structure function human brain. DL, other hand, uses multiple layers interconnected neurons. This enables processing analysis large complex databases. In medicine, these techniques being introduced improve speed efficiency disease diagnosis treatment. Each AI presented paper is supported with an example possible medical application. Given rapid development technology, use medicine shows promising results context patient care. It particularly important keep close eye this issue conduct further research order fully explore potential ML, ANNs, bring applications into clinical future.

Язык: Английский

Процитировано

129
Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare DOI Creative Commons
Tim Hulsen

AI, Год журнала: 2023, Номер 4(3), С. 652 - 666

Опубликована: Авг. 10, 2023

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, deep learning. can be applied in many different areas, econometrics, biometry, e-commerce, the automotive industry. In recent years, has found its way into healthcare well, helping doctors make better decisions (“clinical decision support”), localizing tumors magnetic resonance images, reading analyzing reports written by radiologists pathologists, much more. However, one big risk: it perceived a “black box”, limiting trust reliability, which is very issue an area mean life or death. As result, term Explainable (XAI) been gaining momentum. XAI tries ensure algorithms (and resulting decisions) understood humans. this narrative review, we will have look at some central concepts XAI, describe several challenges around healthcare, discuss whether really help advance, for example, increasing understanding trust. Finally, alternatives increase discussed, well future research possibilities XAI.

Язык: Английский

Процитировано

101
A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations DOI Creative Commons
Zehui Zhao, Laith Alzubaidi, Jinglan Zhang

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 242, С. 122807 - 122807

Опубликована: Дек. 2, 2023

Deep learning has emerged as a powerful tool in various domains, revolutionising machine research. However, one persistent challenge is the scarcity of labelled training data, which hampers performance and generalisation deep models. To address this limitation, researchers have developed innovative methods to overcome data enhance model capabilities. Two prevalent techniques that gained significant attention are transfer self-supervised learning. Transfer leverages knowledge learned from pre-training on large-scale dataset, such ImageNet, applies it target task with limited data. This approach allows models benefit representations effectively new tasks, resulting improved generalisation. On other hand, focuses using pretext tasks do not require manual annotation, allowing them learn valuable large amounts unlabelled These can then be fine-tuned for downstream mitigating need extensive In recent years, found applications fields, including medical image processing, video recognition, natural language processing. approaches demonstrated remarkable achievements, enabling breakthroughs areas disease diagnosis, object understanding. while these offer numerous advantages, they also limitations. For example, may face domain mismatch issues between requires careful design ensure meaningful representations. review paper explores fields within past three years. It delves into advantages limitations each approach, assesses employing techniques, identifies potential directions future By providing comprehensive current methods, article offers guidance selecting best technique specific issue.

Язык: Английский

Процитировано

97
Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges DOI Creative Commons
Yan Chen, Pouyan Esmaeilzadeh

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e53008 - e53008

Опубликована: Март 8, 2024

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding potential uses generative AI health care becomes increasingly important. Generative AI, including models such as adversarial networks large language models, shows promise transforming medical diagnostics, research, treatment planning, patient care. However, these data-intensive systems pose new threats protected information. This Viewpoint paper aims explore various categories care, drug discovery, virtual assistants, clinical decision support, while identifying security privacy within each phase life cycle (ie, data collection, model development, implementation phases). The objectives this study were analyze current state identify opportunities challenges posed by integrating technologies into existing infrastructure, propose strategies for mitigating risks. highlights importance addressing associated with ensure safe effective use systems. findings can inform development future help organizations better understand benefits risks By examining cases across diverse domains contributes theoretical discussions surrounding ethics, vulnerabilities, regulations. In addition, provides practical insights stakeholders looking adopt solutions their organizations.

Язык: Английский

Процитировано

94
Comprehensive systematic review of information fusion methods in smart cities and urban environments DOI Creative Commons
Mohammed A. Fadhel, Ali M. Duhaim, Ahmed Saihood

и другие.

Information Fusion, Год журнала: 2024, Номер 107, С. 102317 - 102317

Опубликована: Фев. 21, 2024

Smart cities result from integrating advanced technologies and intelligent sensors into modern urban infrastructure. The Internet of Things (IoT) data integration are pivotal in creating interconnected spaces. In this literature review, we explore the different methods information fusion used smart cities, along with their advantages challenges. However, there notable challenges managing diverse sources, handling large volumes, meeting near-real-time demands various city applications. review aims to examine applications detail, incorporating quality evaluation techniques identifying critical issues while outlining promising research directions. order accomplish our goal, conducted a comprehensive search applied selective criteria. We identified 59 recent studies addressing machine learning (ML) deep (DL) These were obtained databases such as ScienceDirect (SD), Scopus, Web Science (WoS), IEEE Xplore. main objective study is provide more detailed insights by supplementing existing research. word cloud visualisation learning/deep papers shows landscape, covering both technical aspects artificial intelligence practical settings. Apart exploration, also delves ethical privacy implications arising cities. Moreover, it thoroughly examines that must be addressed realise revolution's potential fully.

Язык: Английский

Процитировано

71
Antecedents of trustworthiness of social commerce platforms: A case of rural communities using multi group SEM & MCDM methods DOI
Sammar Abbas, Alhamzah Alnoor, Sin Yin Teh

и другие.

Electronic Commerce Research and Applications, Год журнала: 2023, Номер 62, С. 101322 - 101322

Опубликована: Окт. 10, 2023

Язык: Английский

Процитировано

67
Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

и другие.

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

Процитировано

53
A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom DOI Creative Commons
Thanveer Shaik, Xiaohui Tao, Lin Li

и другие.

Information Fusion, Год журнала: 2023, Номер 102, С. 102040 - 102040

Опубликована: Сен. 27, 2023

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling comprehensive understanding of patient health and personalized treatment plans. In this paper, journey from to information knowledge wisdom (DIKW) is explored through multimodal for healthcare. We present review focused on the integration various modalities. The explores different approaches such feature selection, rule-based systems, machine ;earning, deep learning, natural language processing, fusing analyzing data. This paper also highlights challenges associated with By synthesizing reviewed frameworks theories, it proposes generic framework that aligns DIKW model. Moreover, discusses future directions related four pillars healthcare: Predictive, Preventive, Personalized, Participatory approaches. components survey presented form foundation more successful implementation Our findings can guide researchers practitioners leveraging power state-of-the-art revolutionize healthcare improve outcomes.

Язык: Английский

Процитировано

52
A review of Explainable Artificial Intelligence in healthcare DOI Creative Commons
Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Çifçi

и другие.

Computers & Electrical Engineering, Год журнала: 2024, Номер 118, С. 109370 - 109370

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

49