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Article: AI-assisted mass spectrometry imaging with

Zhao, Cong-Lin / Mou, Han-Zhang / Pan, Jian-Bin / Xing, Lei / Mo, Yuxiang / Kang, Bin / Chen, Hong-Yuan / Xu, Jing-Juan

2024 Volume 15, Issue 12, Page(s) 4547–4555

Abstract: ... an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with ...

Abstract	Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with
Language	English
Publishing date	2024-02-26
Publishing country	England
Document type	Journal Article
ZDB-ID	2559110-1
ISSN	2041-6539 ; 2041-6520
ISSN (online)	2041-6539
ISSN	2041-6520
DOI	10.1039/d4sc00839a
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: AI Technology panic-is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents.

Huang, Shunsen / Lai, Xiaoxiong / Ke, Li / Li, Yajun / Wang, Huanlei / Zhao, Xinmei / Dai, Xinran / Wang, Yun

Psychology research and behavior management

2024 Volume 17, Page(s) 1087–1102

Abstract: Background: The emergence of new technologies, such as artificial intelligence (AI), may manifest ... the use of AI can lead to AI dependence, which can threaten mental health). While the relationship between ... AI dependence and mental health is a growing topic, the few existing studies are mainly cross ...

Abstract	Background: The emergence of new technologies, such as artificial intelligence (AI), may manifest as technology panic in some people, including adolescents who may be particularly vulnerable to new technologies (the use of AI can lead to AI dependence, which can threaten mental health). While the relationship between AI dependence and mental health is a growing topic, the few existing studies are mainly cross-sectional and use qualitative approaches, failing to find a longitudinal relationship between them. Based on the framework of technology dependence, this study aimed to determine the prevalence of experiencing AI dependence, to examine the cross-lagged effects between mental health problems (anxiety/depression) and AI dependence and to explore the mediating role of AI use motivations. Methods: A two-wave cohort program with 3843 adolescents (Male = 1848, Results: 17.14% of the adolescents experienced AI dependence at T1, and 24.19% experienced dependence at T2. Only mental health problems positively predicted subsequent AI dependence, not vice versa. For AI use motivation, escape motivation and social motivation mediated the relationship between mental health problems and AI dependence whereas entertainment motivation and instrumental motivation did not. Discussion: Excessive panic about AI dependence is currently unnecessary, and AI has promising applications in alleviating emotional problems in adolescents. Innovation in AI is rapid, and more research is needed to confirm and evaluate the impact of AI use on adolescents' mental health and the implications and future directions are discussed.
Language	English
Publishing date	2024-03-12
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2495093-2
ISSN	1179-1578
ISSN	1179-1578
DOI	10.2147/PRBM.S440889
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Article ; Online: AI and Knowledge-Based Method for Rational Design of

Xu, Kangjie / Yu, Shangyang / Wang, Kun / Tan, Yameng / Zhao, Xinyi / Liu, Song / Zhou, Jingwen / Wang, Xinglong

ACS synthetic biology

2024 Volume 13, Issue 1, Page(s) 402–407

Abstract: ... recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method ...

Abstract	Expanding sigma70 promoter libraries can support the engineering of metabolic pathways and enhance recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 promoters. Strong sigma70 promoters were identified by using high-throughput screening (HTS) with enhanced green fluorescent protein (eGFP) as a reporter gene. The features of these strong promoters were adopted to guide promoter design based on our previous reported deep learning model. In the following case study, the obtained strong promoters were used to express collagen and microbial transglutaminase (mTG), resulting in increased expression levels by 81.4% and 33.4%, respectively. Moreover, these constitutive promoters achieved soluble expression of mTG-activating protease and contributed to active mTG expression in
MeSH term(s)	Escherichia coli/genetics ; Escherichia coli/metabolism ; Artificial Intelligence ; DNA-Directed RNA Polymerases/genetics ; Sigma Factor/genetics ; Sigma Factor/metabolism ; Promoter Regions, Genetic/genetics
Chemical Substances	DNA-Directed RNA Polymerases (EC 2.7.7.6) ; Sigma Factor
Language	English
Publishing date	2024-01-04
Publishing country	United States
Document type	Journal Article
ISSN	2161-5063
ISSN (online)	2161-5063
DOI	10.1021/acssynbio.3c00578
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Book ; Online: Self Generated Wargame AI

Sun, Y. / Zhao, J. / Yu, C. / Wang, W. / Zhou, X.

Double Layer Agent Task Planning Based on Large Language Model

2023

Abstract: ... of the large language model is significantly stronger than the commonly used reinforcement learning AI and rule ... AI, and the intelligence, understandability and generalization are all better. And ...

Abstract	The large language models represented by ChatGPT have a disruptive impact on the field of artificial intelligence. But it mainly focuses on natural language processing, speech recognition, machine learning and natural language understanding. This paper innovatively applies the large language model to the field of intelligent decision-making, places the large language model in the decision-making center, and constructs an agent architecture with the large language model as the core. Based on this, it further proposes a two-layer agent task planning, issues and executes decision commands through the interaction of natural language, and carries out simulation verification through the wargame simulation environment. Through the game confrontation simulation experiment, it is found that the intelligent decision-making ability of the large language model is significantly stronger than the commonly used reinforcement learning AI and rule AI, and the intelligence, understandability and generalization are all better. And through experiments, it was found that the intelligence of the large language model is closely related to prompt. This work also extends the large language model from previous human-computer interaction to the field of intelligent decision-making, which has important reference value and significance for the development of intelligent decision-making.
Keywords	Computer Science - Artificial Intelligence ; Computer Science - Computation and Language
Publishing date	2023-12-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Towards a Psychological Generalist AI

He, Tianyu / Fu, Guanghui / Yu, Yijing / Wang, Fan / Li, Jianqiang / Zhao, Qing / Song, Changwei / Qi, Hongzhi / Luo, Dan / Zou, Huijing / Yang, Bing Xiang

A Survey of Current Applications of Large Language Models and Future Prospects

2023

Abstract: ... AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm ... of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models ... AI models. These future generalist AI models harbor the potential to substantially curtail labor ...

Abstract	The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications.
Keywords	Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Machine Learning
Publishing date	2023-12-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Siren's Song in the AI Ocean

Zhang, Yue / Li, Yafu / Cui, Leyang / Cai, Deng / Liu, Lemao / Fu, Tingchen / Huang, Xinting / Zhao, Enbo / Zhang, Yu / Chen, Yulong / Wang, Longyue / Luu, Anh Tuan / Bi, Wei / Shi, Freda / Shi, Shuming

A Survey on Hallucination in Large Language Models

2023

Abstract: While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the ... ...

Abstract	While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research. Comment: work in progress; 32 pages
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society ; Computer Science - Machine Learning
Subject code	501
Publishing date	2023-09-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A survey of recent methods for addressing AI fairness and bias in biomedicine.

Yang, Yifan / Lin, Mingquan / Zhao, Han / Peng, Yifan / Huang, Furong / Lu, Zhiyong

ArXiv

2024

Abstract: Objectives: Artificial intelligence (AI) systems have the potential to revolutionize clinical ... or after the development of AI models, making it critical to understand and address potential biases ... to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns ...

Abstract	Objectives: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. Methods: We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. Results: The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.
Language	English
Publishing date	2024-02-13
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: A survey of recent methods for addressing AI fairness and bias in biomedicine.

Yang, Yifan / Lin, Mingquan / Zhao, Han / Peng, Yifan / Huang, Furong / Lu, Zhiyong

Journal of biomedical informatics

2024 , Page(s) 104646

Abstract	Objectives: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. Methods: We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. Results: The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.
Language	English
Publishing date	2024-04-25
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2024.104646
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Article ; Online: Bias of AI-generated content: an examination of news produced by large language models.

Fang, Xiao / Che, Shangkun / Mao, Minjia / Zhang, Hongzhe / Zhao, Ming / Zhao, Xiaohang

Scientific reports

2024 Volume 14, Issue 1, Page(s) 5224

Abstract: ... they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand ...

Abstract	Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.
MeSH term(s)	Humans ; Female ; Male ; Animals ; Sexism ; Bias ; Aortic Valve Insufficiency ; Camelids, New World ; Language
Language	English
Publishing date	2024-03-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-55686-2
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Article ; Online: Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Xu, WenWen / Jia, XiaoHong / Mei, ZiHan / Gu, XiaoLin / Lu, Yang / Fu, Chi-Cheng / Zhang, RuiFang / Gu, Ying / Chen, Xia / Luo, XiaoMao / Li, Ning / Bai, BaoYan / Li, QiaoYing / Yan, JiPing / Zhai, Hong / Guan, Ling / Gong, Bing / Zhao, KeYang / Fang, Qu /

He, Chuan / Zhan, WeiWei / Luo, Ting / Zhang, HuiTing / Dong, YiJie / Zhou, JianQiao

Radiology

2023 Volume 307, Issue 5, Page(s) e221157

Abstract: Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules ... however, the lack of generalizability limits the application of these models. Purpose To develop AI models ... multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and ...

Abstract	Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (
MeSH term(s)	Humans ; Female ; Middle Aged ; Artificial Intelligence ; Thyroid Nodule/diagnostic imaging ; Retrospective Studies ; Thyroid Neoplasms
Language	English
Publishing date	2023-06-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.221157
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