A top-down outlook on artificial intelligence applied to healthcare systems and possible advantage of an unsupervised learning tool to medical issues

2020 ◽  
Vol 5 (4) ◽  
pp. 276
Author(s):  
V. González Prida ◽  
J. Zamora
Keyword(s):  
Artificial Intelligence ◽  
Unsupervised Learning ◽  
Healthcare Systems ◽  
Top Down ◽  
Learning Tool ◽  
Medical Issues

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.

A Survey on Ontology Creation Methodologies

2011 ◽  
pp. 98-122 ◽  
Author(s):  
Matteo Cristani ◽  
Roberta Cuel
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Information Systems ◽  
Current Literature ◽  
A Priori ◽  
Top Down ◽  
Specific Context ◽  
Systematic Analysis ◽  
General Terms ◽  
The Right

In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the description of the domain itself; (2) organizing the terms into a taxonomy of the classes by the ISA relation; and (3) expressing in an explicit way the constraints that make the ISA pairs meaningful. Though a number of such approaches can be found, no systematic analysis of them exists which can be used to understand the inspiring motivation, the applicability context, and the structure of the approaches. In this paper, we provide a framework for analyzing the existing methodologies that compares them to a set of general criteria. In particular, we obtain a classification based upon the direction of ontology construction; bottom-up are those methodologies that start with some descriptions of the domain and obtain a classification, while top-down ones start with an abstract view of the domain itself, which is given a priori. The resulting classification is useful not only for theoretical purposes but also in the practice of deployment of ontologies in Information Systems, since it provides a framework for choosing the right methodology to be applied in the specific context, depending also on the needs of the application itself.

scholarly journals Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X

AI and Ethics ◽  
2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Carsten Maple ◽  
Uchenna Ani
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Qualitative Case Study ◽  
Healthcare Systems ◽  
Lessons Learned ◽  
Case Study Method ◽  
Research Problems ◽  
Systems Learning ◽  
Digital Healthcare

AbstractArtificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology—that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

scholarly journals Smart Technologies Driven approaches to tackle COVID-19 Pandemic

2020 ◽  
Author(s):  
Muni Raj Maurya ◽  
Kishor Kumar Sadasivuni ◽  
Sumaya Ali S A Al-Maadeed
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Severe Acute Respiratory Syndrome ◽  
Data Communication ◽  
Healthcare Systems ◽  
Government Policies ◽  
Smart Sensors ◽  
Public Health Issue ◽  
Corona Virus ◽  
Smart Technologies

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) had led to a global outbreak of Coronavirus Disease-2019 (COVID-19) and raised an international public health issue. To mitigate the infection and bring the sustainability in current pandemic situation, the healthcare system and governments are doing exceptional work. Globally, the implementation of technologies in healthcare systems and diverse government policies has proven to be effective in tackling COVID-19. The rapid technological swift during the pandemic and its role in assisting the fight against corona virus is phenomenal. Various technologies like robotics, drone, artificial intelligence (AI), data communication, mask, and smart sensors, etc. has synergistically helped in mitigating the effect of COVID-19. The poster represents the outlook of these technologies in terms of strategies and framework in which they have been applied for assisting various sectors like the health system, industries, government, and public, etc.

scholarly journals Ai Consulting Healthcare Chatbot System Using Pattern Matching

2021 ◽  
pp. 18-22
Author(s):  
G. Nivedhitha ◽  
E. Punarselvam ◽  
K. R. Aaghash ◽  
M. Elayabarathi ◽  
K. Rahul ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pattern Matching ◽  
Health Problem ◽  
Right Action ◽  
Medical Issues ◽  
The People ◽  
The Right

In today's world there are millions of diseases with various symptoms foreach, no human can possibly know about all of these diseases and the treatmentsassociated with them. So, the problem is that there isn’t any place where anyone can have the details of the diseases or the medicines/treatments. What if there is a placewhere you can find your health problem just by entering symptoms or the currentcondition of the person. It will help us to deduce the problem and to verify thesolution. The proposed idea is to create a system with artificial intelligence that canmeet these requirements. The AI can classify the diseases based on the symptomsand give the list of available treatments. The System is a text-to-text diagnosis chatbot that will engage patients in conversation with their medical issues and provides apersonalized diagnosis based on their symptoms and profile. Hence the people canhave an idea about their health and can take the right action.

scholarly journals Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

2021 ◽  
Vol 118 (15) ◽  
pp. e2016239118
Author(s):  
Alexander Rives ◽  
Joshua Meier ◽  
Tom Sercu ◽  
Siddharth Goyal ◽  
Zeming Lin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Amino Acids ◽  
Unsupervised Learning ◽  
Tertiary Structure ◽  
State Of The Art ◽  
Sequence Data ◽  
Protein Sequences ◽  
Representation Learning ◽  
Biological Properties ◽  
Biochemical Properties

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction.

scholarly journals Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic

2021 ◽  
Vol 4 ◽  
Author(s):  
Jay Carriere ◽  
Hareem Shafi ◽  
Katelyn Brehon ◽  
Kiran Pohar Manhas ◽  
Katie Churchill ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Chronic Conditions ◽  
Healthcare Delivery ◽  
Occupational Therapists ◽  
Healthcare Systems ◽  
Research Development ◽  
Policy Makers ◽  
Neurological Conditions

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

scholarly journals Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

2019 ◽  
Author(s):  
Alexander Rives ◽  
Joshua Meier ◽  
Tom Sercu ◽  
Siddharth Goyal ◽  
Zeming Lin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Amino Acids ◽  
Unsupervised Learning ◽  
Tertiary Structure ◽  
State Of The Art ◽  
Sequence Data ◽  
Protein Sequences ◽  
Representation Learning ◽  
Biological Properties ◽  
Biochemical Properties

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state-of-the-art features for long-range contact prediction.

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