AI-enhanced biomedical micro/nanorobots in microfluidics

Dong, Hui; Lin, Jiawen; Tao, Yihui; Jia, Yuan; Sun, Lining; Li, Wen Jung; Sun, Hao

doi:10.1039/d3lc00909b

Lab Chip

2024

DOI: 10.1039/d3lc00909b

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AI-enhanced biomedical micro/nanorobots in microfluidics

Hui Dong,

Jiawen Lin,

Yihui Tao

et al.

Abstract: Although developed independently at the beginning, AI, micro/nanorobots and microfluidics have become more intertwined in the past few years which has greatly propelled the cutting-edge development in fields of biomedical sciences.

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Cited by 9 publications

References 211 publications

(237 reference statements)

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Advancements in Machine Learning for Microrobotics in Biomedicine

Salehi,

Hosseinpour,

Tabatabaei

et al. 2024

Advanced Intelligent Systems

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Microrobotics, particularly in the field of biomedicine, has garnered considerable attention due to its potential for noninvasive medical interventions enabled by the small size of microrobots. However, controlling and imaging them present unique challenges compared to their macroscale counterparts, primarily due to the intricate anatomical spaces and dynamic environments within the human body. Existing imaging modalities also face limitations, hindering real‐time visualization and control of microrobots in deep tissue. Machine learning (ML) algorithms offer promising solutions to these challenges by enabling adaptive motion control and enhancing image resolution through robust data analysis and decision‐making capabilities. In this review, a comprehensive overview of recent advancements in ML‐based techniques for microrobotic research is provided, emphasizing their applications in imaging and control in biomedical contexts. Additionally, current obstacles and potential future directions for ML algorithms in microrobotics, particularly regarding their translation to clinical settings, are discussed.

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Advancements in Machine Learning for Microrobotics in Biomedicine

Salehi,

Hosseinpour,

Tabatabaei

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

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Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications

Shaw,

Ali,

Nie

et al. 2024

Advanced Intelligent Systems

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Microfluidics has evolved into a transformative technology with far‐reaching applications in biomedical research. However, designing and optimizing custom microfluidic systems remains challenging because of their inherent complexities. Integrating artificial intelligence (AI) with microfluidics promises to overcome these barriers by leveraging AI algorithms to automate device design, streamline experimentation, and enhance diagnostic and therapeutic outcomes. Psoriasis is an incurable dermatological condition that is difficult to diagnose and treat owing to its complex pathogenesis. Traditional diagnostic and therapeutic approaches are often ineffective and fail to address individual variabilities in disease progression and treatment responses. However, AI‐coupled microfluidic platforms have the potential to revolutionize psoriasis research and clinical applications with expansive dermatological applications. AI‐driven microfluidic chips with embedded biosensors have the potential to precisely detect biomarkers (BMs), manipulate biological samples, and mimic psoriasis‐like in vivo and in vitro models, thereby allowing real‐time monitoring and optimized therapeutic testing. This review examines the transformative potential of AI and AI‐powered microfluidic platforms for advancing psoriasis research. It examines the design and mechanisms of AI‐coupled microfluidic platforms for cell screening, disease diagnosis, and drug delivery. It highlights recent advances, clinical applications, challenges, future perspectives, and ethical considerations to enhance personalized care and patient outcomes.

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Evaluation of Salophen-Based Immobilized Copper Nanoparticles Biosynthesized Using Curcuma Longa Extract: Physicochemical Characterization and Biological Study

Haghighi,

Ghamari kargar,

Bagherzade

et al. 2024

Inorganic Chemistry Communications

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AI-enhanced biomedical micro/nanorobots in microfluidics

Abstract: Although developed independently at the beginning, AI, micro/nanorobots and microfluidics have become more intertwined in the past few years which has greatly propelled the cutting-edge development in fields of biomedical sciences.

Cited by 9 publications

References 211 publications

Advancements in Machine Learning for Microrobotics in Biomedicine

Advancements in Machine Learning for Microrobotics in Biomedicine

Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications

Evaluation of Salophen-Based Immobilized Copper Nanoparticles Biosynthesized Using Curcuma Longa Extract: Physicochemical Characterization and Biological Study

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