Robotic Motion Planning Using Probabilistic Collision Hidden Markov Model (PCHMM) for Contactless Drug Delivery

Khakharia, Aman; Mehendale, Ninad

doi:10.2139/ssrn.3860266

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…HelpMate is a mature solution that provides autonomous transportation of materials and supplies, with users dispatching tasks through their console interface, and autonomous operations with unsupervised navigation technologies, such as proximity sensors for obstacle avoidance and path planning for navigation [ 48 ]. In the ICU, the use of automated transport vehicles will increase efficiency and avoid the potential for cross-infection, especially during the COVID-19 lockdown [ 82 ].…”

Section: Resultsmentioning

confidence: 99%

Advances in the Application of AI Robots in Critical Care: Scoping Review

Li,

Wang,

Wang

et al. 2024

J Med Internet Res

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Background In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. Objective This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. Methods Following the scoping review framework proposed by Arksey and O’Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. Results Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. Conclusions This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

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Section: Resultsmentioning

confidence: 99%

Advances in the Application of AI Robots in Critical Care: Scoping Review

Li,

Wang,

Wang

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

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Advances in the Application of AI Robots in Critical Care: Scoping Review (Preprint)

Li,

Wang,

Wang

et al. 2023

Preprint

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UNSTRUCTURED In recent epochs, the field of critical medicine has seen a plethora of advancements due to the advent of artificial intelligence. Foremost among these is the evolution of artificial intelligence robot, which has progressed from the conceptual phase to implementation in clinical trials and applications. The Intensive Care Unit (ICU), a highly information-based medical facilities, is one of the most auspicious platforms for robotic AI applications and poised to derive substantial benefits. However, the reports of AI robotic usage in ICUs remains scarce. This review undertakes an extensive literature exploration of the development and usage of AI robots in ICUs, classifying them into five broad categories based on functionality and deployment scenarios: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, logistics and disinfection robots. Moreover, it encapsulates ethical conundrums associated with AI robot utilization in ICU scenarios such as safety apprehensions, patient privacy and confidentiality, delineation of responsibility and cost-benefit issues. We propose plausible strategies to circumvent these issues and offer suggestions for the broader application of AI robots in ICUs.

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Robotic Motion Planning Using Probabilistic Collision Hidden Markov Model (PCHMM) for Contactless Drug Delivery

Cited by 2 publications

References 19 publications

Advances in the Application of AI Robots in Critical Care: Scoping Review

Advances in the Application of AI Robots in Critical Care: Scoping Review

Advances in the Application of AI Robots in Critical Care: Scoping Review (Preprint)

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