Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

Klumpp, Matthias; Hintze, Marcus; Immonen, Milla; Ródenas-Rigla, Francisco; Pilati, Francesco; Aparicio-Martínez, Fernando; Çelebi, Dilay; Liebig, Thomas; Jirstrand, Mats; Urbann, Oliver; Hedman, Marja; Lipponen, Jukka A.; Bicciato, Silvio; Radan, Anda‐Petronela; Valdivieso, Bernardo; Thronicke, Wolfgang; Gunopulos, Dimitrios; Delgado-Gonzalo, Ricard

doi:10.3390/healthcare9080961

Cited by 29 publications

(18 citation statements)

References 41 publications

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“…Additionally, AI will be useful in workflow optimization and efficiency, helping eliminate redundancy in cost from duplicate or unnecessary procedures [56,57] .…”

Section: Discussionmentioning

confidence: 99%

“…By analyzing patient data from medical and medication errors, readmission root causes, and other internal and external databases, AI will one day identify and prevent high-risk patients from developing complications, and provide prospective care guidance, and diagnostic support, among many other clinical applications. Additionally, AI will be useful in workflow optimization and efficiency, helping eliminate redundancy in cost from duplicate or unnecessary procedures [ 56 , 57 ] .…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

et al. 2022

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Artificial Intelligence (AI) emerged as an intervention for data and number-related problems. This breakthrough has led to several technological advancements in virtually all fields from engineering to architecture, education, accounting, business, health, and so on. AI has come a long way in healthcare, having played significant roles in data and information storage and management – such as patient medical histories, medicine stocks, sale records, and so on; automated machines; software and computer applications like diagnostic tools such as MRI radiation technology, CT diagnosis and many more have all been created to aid and simplify healthcare measures. Inarguably, AI has revolutionized healthcare to be more effective and efficient and the pharmacy sector is not left out. During the past few years, a considerable amount of increasing interest in the uses of AI technology has been identified for analyzing as well as interpreting some important fields of pharmacy like drug discovery, dosage form designing, polypharmacology, and hospital pharmacy. Given the growing importance of AI, we wanted to create a comprehensive report which helps every practicing pharmacist understand the biggest breakthroughs which are assisted by the deployment of this field.

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“…Additionally, AI will be useful in workflow optimization and efficiency, helping eliminate redundancy in cost from duplicate or unnecessary procedures [56,57] .…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

et al. 2022

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“…The healthcare system should provide privacy and security at the same time [ 46 , 47 ]. Normally, only anonymity is considered to provide privacy of PT in some other protocols in [ 40 , 41 ].…”

Section: Preliminariesmentioning

confidence: 99%

Privacy-Preserving Authentication Protocol for Wireless Body Area Networks in Healthcare Applications

Ryu

Kim

2021

Healthcare

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Mobile healthcare service has become increasingly popular thanks to the significant advances in the wireless body area networks (WBANs). It helps medical professionals to collect patient’s healthcare data remotely and provides remote medical diagnosis. Since the health data are privacy-related, they should provide services with privacy-preserving, which should consider security and privacy at the same time. Recently, some lightweight patient healthcare authentication protocols were proposed for WBANs. However, we observed that they are vulnerable to tracing attacks because the patient uses the same identifier in each session, which could leak privacy-related information on the patient. To defeat the weakness, this paper proposes a privacy-preserving authentication protocol for WBANs in healthcare service. The proposed protocol is only based on one-way hash function and with exclusive-or operation, which are lightweight operations than asymmetric cryptosystem operations. We performed two rigorous formal security proofs based on BAN logic and ProVerif tool. Furthermore, comparison results with the relevant protocols show that the proposed protocol achieves more privacy and security features than the other protocols and has suitable efficiency in computational and communicational concerns.

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“…Inferring phone positions with activity can be effective for inferring the habitual behavior of a person in different contexts. Thus, the proposed scheme can be extended to detect and recognize normal/abnormal human behavior, which can further be advantageous in predicting/avoiding health-related risks, as discussed in the existing studies [41][42][43]. In addition, the proposed scheme can serve as a building block for recommender systems and context-aware computing applications.…”

Section: Introductionmentioning

confidence: 99%

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

et al. 2022

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Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.

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Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

Cited by 29 publications

References 41 publications

Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

Privacy-Preserving Authentication Protocol for Wireless Body Area Networks in Healthcare Applications

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

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