Artificial Intelligence-Powered Diagnostic Tools, Networked Medical Devices, and Cyber-Physical Healthcare Systems in Assessing and Treating Patients with COVID-19 Symptoms

2021 ◽  
Vol 8 (2) ◽  
pp. 91
Keyword(s):  
Artificial Intelligence ◽  
Medical Devices ◽  
Healthcare Systems ◽  
Diagnostic Tools

Clinical Cybersecurity Training Through Novel High Fidelity Simulations (Preprint)

2018 ◽  
Author(s):  
Christian Dameff ◽  
Jordan Selzer ◽  
Jonathan Fisher ◽  
James Killeen ◽  
Jeffrey Tully
Keyword(s):  
Patient Safety ◽  
Patient Care ◽  
Medical Devices ◽  
Healthcare Systems ◽  
High Fidelity ◽  
Protected Health Information ◽  
Patient Harm ◽  
Security Vulnerabilities ◽  
Clinical Simulations ◽  
Medical Infrastructure

BACKGROUND Cybersecurity risks in healthcare systems have traditionally been measured in data breaches of protected health information but compromised medical devices and critical medical infrastructure raises questions about the risks of disrupted patient care. The increasing prevalence of these connected medical devices and systems implies that these risks are growing. OBJECTIVE This paper details the development and execution of three novel high fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices. METHODS Clinical simulations were developed which incorporated patient care scenarios with hacked medical devices based on previously researched security vulnerabilities. RESULTS Clinician participants universally failed to recognize the etiology of their patient’s pathology as being the result of a compromised device. CONCLUSIONS Simulation can be a useful tool in educating clinicians in this new, critically important patient safety space.

scholarly journals 人工智能醫學應用的前景與風險

2019 ◽  
Vol 17 (2) ◽  
pp. 79-109
Author(s):  
Robert SPARROW ◽  
Joshua HATHERLEY
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Health Care ◽  
Healthcare Systems ◽  
Business Practices ◽  
Medical Decisions ◽  
Diagnostic Aid ◽  
Healthcare Settings ◽  
Digital Commons ◽  
Distribution Of Power

LANGUAGE NOTE | Document text in English; abstract also in Chinese.人工智能（ＡＩ）將如何促進人類的醫療保健？如果我們擔心人工智能介入醫療的風險，我們又應該關注什麽呢？本文試圖概述此類問題，並對人工智能介入醫療的風險與希望作一個初步評價。人工智能作為一種研究工具和診斷工具具有巨大的潛力，特別是在基因組學和公共衛生領域中。人工智能在醫療中的廣泛使用可能還會對醫療系統的組織方式和商業實踐產生深刻的影響，而這些影響的方式與程度還沒有被充分認識到。在人工智能醫學的熱情擁護者看來，應用人工智能可以幫助醫生集中精力在對他們和病人而言真正重要的問題上。然而，本文將論證這些樂觀的判斷是基於對現代醫療環境下機構和經濟運行規則的一些不合情理的假設之上。本文將聚焦於如下一 些重要議題：大資料中的隱私、監管和偏見，過分信任機器的風險，透明度問題，醫療專業人士的“去技能化”問題，人工智能重塑醫療保健的方式，以及人工智能對醫療保健中權力分配的影響。其中有兩個關鍵的問題尤其值得哲學家和生命倫理學家的進一步關注。第一，當醫生不僅需要處理人而且需要處理資料的時候，醫療實踐會呈現出什麽樣的形態？第二，在醫療決策權衡中，我們應該给予來自機器的意見以多大的權重？What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It’s also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?DOWNLOAD HISTORY | This article has been downloaded 119 times in Digital Commons before migrating into this platform.

Artificial Intelligence in Diagnosis and Prediction of the Multiple Sclerosis Progression: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Ghassan Saad ◽  
Bassam Jaber ◽  
Maryam Al-Hajri ◽  
Mowafa Househ ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Multiple Sclerosis ◽  
Selection Process ◽  
Data Extraction ◽  
Clinical History ◽  
Diagnostic Tools ◽  
Bibliographic Databases ◽  
Data Synthesis ◽  
Study Selection ◽  
Development Plans

BACKGROUND Multiple Sclerosis (MS) is an autoimmune disease that results from the demyelination of the nerves in the Central Nervous System. The diagnosis depends on clinical history, neurological examination, and radiological images. Artificial Intelligence proved to be an effective tool in enhancing the diagnostic tools of MS. OBJECTIVE To explore how AI assisted in diagnosis and predicting the progression of MS. METHODS We used three bibliographic databases in our search: PubMed IEEE Xplore and Cochrane in our search. The study selection process included: removal of duplicated articles, screening titles and abstracts, and reading the full text. This process was performed by two reviewers. The data extracted from the included studies have been filled in an Excel sheet. This step had been done by each reviewer accordingly to the assigned articles. The extracted data sheet was checked by two reviewers to have accuracy ensured. The narrative approach is applied in data synthesis. RESULTS The search conducted resulted in 320 articles Removing duplicates and excluding the ineligible articles due to irrelevancy to the population, intervention, and outcomes resulted in excluding 299 articles. Thus, our review will include 21 articles for data extraction and data synthesis. CONCLUSIONS Artificial Intelligence is becoming a trend in the medical field. Its contribution in enhancing the diagnostic tools of many diseases, as in MS, is prominent and can be built on in further development plans. However, the implementation of Artificial Intelligence in Multiple Sclerosis is not widespread to confirm the benefits gained, and the datasets involved in the current practice are relatively small. It is recommended to have more studies that focus on the relationship between the employment of AI in diagnosis and monitoring progression and the accuracy gained by this employment.

Healthcare IoT Architectures, Technologies, Applications, and Issues

2019 ◽  
pp. 235-265 ◽  
Author(s):  
Karthick G. S. ◽  
Pankajavalli P. B.
Keyword(s):  
Medical Devices ◽  
Healthcare Systems ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Layered Architecture ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
The Internet Of Things ◽  
Human Healthcare ◽  
Patient Centric

The internet of things (IoT) revolution is improving the proficiency of human healthcare infrastructures, and this chapter analyzes the applications of IoT in healthcare systems with diversified aspects such as topological arrangement of medical devices, layered architecture, and platform services. This chapter focuses on advancements in IoT-based healthcare in order to identify the communication and sensing technologies enabling the smart healthcare systems. The transformation of healthcare from doctor-centric to patient-centric with the diversified applications of IoT is discussed in detail. In addition, this chapter examines the various issues to be emphasized on designing an effective IoT-based healthcare system. It also explores security in healthcare systems and the possible security threats that may be vulnerable to the security essentials. Finally, this chapter summarizes the procedure of applying machine learning techniques on healthcare streaming data which provides intelligence to the systems.

scholarly journals Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3532 ◽  
Author(s):  
Ryuji Hamamoto ◽  
Kruthi Suvarna ◽  
Masayoshi Yamada ◽  
Kazuma Kobayashi ◽  
Norio Shinkai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Medical Devices ◽  
The United States ◽  
Medical Data ◽  
Clinical Implementation ◽  
Medical Field ◽  
Core Technology ◽  
Artificial Intelligence Technology ◽  
History Of

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

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