scholarly journals Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey

2020 ◽  
Vol 6 ◽  
pp. 205520762096835
Author(s):  
C Blease ◽  
C Locher ◽  
M Leon-Carlyle ◽  
M Doraiswamy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Profession ◽  
Descriptive Analysis ◽  
Psychiatric Practice ◽  
Limited Attention ◽  
Written Responses ◽  
Future Technology ◽  
Qualitative Descriptive ◽  
The Impact

Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. Objective This study aimed to explore psychiatrists’ opinions about the potential impact innovations in artificial intelligence and machine learning on psychiatric practice Methods In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written responses (“comments”) to three open-ended questions in the survey. Results Comments were classified into four major categories in relation to the impact of future technology on: (1) patient-psychiatrist interactions; (2) the quality of patient medical care; (3) the profession of psychiatry; and (4) health systems. Overwhelmingly, psychiatrists were skeptical that technology could replace human empathy. Many predicted that ‘man and machine’ would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. Conclusions This study presents timely information on psychiatrists’ views about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

scholarly journals Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views (Preprint)

2018 ◽  
Author(s):  
Charlotte Blease ◽  
Ted J Kaptchuk ◽  
Michael H Bernstein ◽  
Kenneth D Mandl ◽  
John D Halamka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
General Practitioners ◽  
Medical Profession ◽  
Descriptive Analysis ◽  
Written Responses ◽  
Ethical Concerns ◽  
Future Technology ◽  
Qualitative Work ◽  
Value Based Care

BACKGROUND The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields. OBJECTIVE This study aimed to explore general practitioners’ (GPs’) opinions about the potential impact of future technology on key tasks in primary care. METHODS In June 2018, we conducted a Web-based survey of 720 UK GPs’ opinions about the likelihood of future technology to fully replace GPs in performing 6 key primary care tasks, and, if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. This study involved qualitative descriptive analysis of written responses (“comments”) to an open-ended question in the survey. RESULTS Comments were classified into 3 major categories in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning and the ability to provide value-based care as necessitating physicians’ judgments. Perceived benefits of technology included expectations about improved efficiencies, in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. CONCLUSIONS This study presents timely information on physicians’ views about the scope of artificial intelligence (AI) in primary care. Overwhelmingly, GPs considered the potential of AI to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views.

scholarly journals Systematic review and bibliometric analysis of African anesthesia and critical care medicine research part II: a scientometric analysis of the 116 most cited articles

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ulrick Sidney Kanmounye ◽  
Joel Noutakdie Tochie ◽  
Aimé Mbonda ◽  
Cynthia Kévine Wafo ◽  
Leonid Daya ◽  
...  
Keyword(s):  
Critical Care ◽  
South African ◽  
Web Of Science ◽  
Descriptive Analysis ◽  
World Journal ◽  
Critical Care Medicine ◽  
Scientometric Analysis ◽  
Medicine Research ◽  
Qualitative Descriptive ◽  
The Impact

Abstract Background Scientometrics is used to assess the impact of research in several health fields, including Anesthesia and Critical Care Medicine. The purpose of this study was to identify contributors to highly-cited African Anesthesia and Critical Care Medicine research. Methods The authors searched Web of Science from inception to May 4, 2020, for articles on and about Anesthesia and Critical Care Medicine in Africa with ≥2 citations. Quantitative (H-index) and qualitative (descriptive analysis of yearly publications and interpretation of document, co-authorship, author country, and keyword) bibliometric analyses were done. Results The search strategy returned 116 articles with a median of 5 (IQR: 3–12) citations on Web of Science. Articles were published in Anesthesia and Analgesia (18, 15.5%), World Journal of Surgery (13, 11.2%), and South African Medical Journal (8, 6.9%). Most (74, 63.8%) articles were published on or after 2013. Seven authors had more than 1 article in the top 116 articles: Epiu I (3, 2.6%), Elobu AE (2, 1.7%), Fenton PM (2, 1.7%), Kibwana S (2, 1.7%), Rukewe A (2, 1.7%), Sama HD (2, 1.7%), and Zoumenou E (2, 1.7%). The bibliometric coupling analysis of documents highlighted 10 clusters, with the most significant nodes being Biccard BM, 2018; Baker T, 2013; Llewellyn RL, 2009; Nigussie S, 2014; and Aziato L, 2015. Dubowitz G (5) and Ozgediz D (4) had the highest H-indices among the authors referenced by the most-cited African Anesthesia and Critical Care Medicine articles. The U.S.A., England, and Uganda had the strongest collaboration links among the articles, and most articles focused on perioperative care. Conclusion This study highlighted trends in top-cited African articles and African and non-African academic institutions’ contributions to these articles.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals Early Adopters to Early Majority - What's Driving the Artificial Intelligence and Machine Learning Powered Transformation in Financial Services?

2021 ◽  
Vol 12 (4) ◽  
pp. 43
Author(s):  
Srikrishna Chintalapati
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Portfolio Management ◽  
Value Chain ◽  
Financial Services ◽  
Retail Banking ◽  
Early Adopters ◽  
Customer Life Cycle ◽  
Digital Disruption ◽  
The Impact

From retail banking to corporate banking, from property and casualty to personal lines, and from portfolio management to trade processing, the next wave of digital disruption in financial services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest technological transformations the world has ever witnessed. Within the advanced streams of research in AI and ML, human intelligence blended with the cognitive reasoning of machines is finally out of the labs and into real-time applications. The Financial Services sector is one of the early adopters of this revolution and arguably much ahead of its leverage compared to other sectors. Built on the conceptual foundations of Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey across the AI-value chain defined by McKinsey Global Institute (2017), the current study attempts to highlight the features and use-cases of early-adopters of this transformation. With the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt to comment on how AI and ML have been significantly transforming the Financial Services market space from the lens of a domain practitioner. The findings of this study would be of particular relevance to the subject matter experts, Industry analysts, academicians, and researchers focussed on studying the impact of AI and ML in the financial services industry.

scholarly journals Program Pemulihan Ekonomi Nasional Sebagai Kebijakan Penanggulangan Dampak Pandemi Covid-19 Pada Sektor UMKM

2021 ◽  
Vol 8 (2) ◽  
pp. 110-116
Author(s):  
Ratnawaty Marginingsih
Keyword(s):  
Positive Impact ◽  
Descriptive Analysis ◽  
Economic Recovery ◽  
Analysis Technique ◽  
National Economic ◽  
Qualitative Descriptive ◽  
The Government ◽  
Recovery Program ◽  
The Impact ◽  
Economic Recovery Program

Abstrak  Berbagai permasalahan yang terjadi pada UMKM terdampak pandemi cukup dirasakan oleh para pelaku usaha tersebut. Hal ini tentu saja berakibat pada penurunan keuntungan secara signifikan dikarenanakan tingkat produktivitas yang rendah. Langkah terkait pemulihan ekonomi, dalam hal ini pemerintah melalui kementrian keuangan membuat kebijakan luar biasa untuk memitigasi dampak covid-19 dan perlambatan ekonomi dengan membuat Program Pemulihan Ekonomi Nasional (PEN).  Metode penelitian yang digunakan dalam penulisan ini adalah teknik analisis deskriptif kualitatif. Hasil penelitian menujukkan Program Pemulihan Ekonomi Nasional (PEN) memiliki dampak positif bagi sektor UMKM pada masa pandemi covid-19 sebagai langkah kebijakan yang diambil oleh pemerintah untuk mendukung pemulihan perekonomian nasional khususnya sektor UMKM yang memiliki kontribusi cukup besar. Rekomendasi kebijkan penguatan UMKM tidak hanya pada masa pandemi tetapi juga pada masa pemulihan dan pasca pandemi sehingga percepatan pemulihan ekonomi nasional dapat mencapai kestabilannya. Kata Kunci: Program PEN, Kebijkan Pandemi, UMKM  Abstract - The various problems that occur in SMEs affected by the pandemic are quite felt by these business actors. This of course results in a significant reduction in profits due to low productivity levels. Steps related to economic recovery, in this case, the government through the ministry of finance, make extraordinary policies to mitigate the impact of covid-19 and the economic slowdown by creating the National Economic Recovery Program (PEN). The research method used in this paper is a qualitative descriptive analysis technique. The results of the study show that the National Economic Recovery Program (PEN) has a positive impact on the MSME sector during the COVID-19 pandemic as a policy step taken by the government to support the recovery of the national economy, especially the MSME sector which has a significant contribution. Recommendations for strengthening MSME policies are not only during the pandemic but also during the recovery and post-pandemic period so that the acceleration of national economic recovery can achieve stability. Keywords: PEN Program, Pandemic Policy, MSME 

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity

2019 ◽  
Vol 30 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Weiyu Wang ◽  
Keng Siau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Future Research ◽  
Way Of Life ◽  
Negative Effects ◽  
Future Research Directions ◽  
Negative Impacts ◽  
The Impact ◽  
The Way

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

scholarly journals Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

2019 ◽  
Vol 33 (2) ◽  
pp. 31-50 ◽  
Author(s):  
Ajay Agrawal ◽  
Joshua S. Gans ◽  
Avi Goldfarb
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Labor Market ◽  
Market Impact ◽  
Recent Advances ◽  
The Impact ◽  
Decision Tasks

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

scholarly journals Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists (Preprint)

2019 ◽  
Author(s):  
Nicoletta Musacchio ◽  
Annalisa Giancaterini ◽  
Giacomo Guaita ◽  
Alessandro Ozzello ◽  
Maria A Pellegrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Diabetes Care ◽  
Digital Health ◽  
Descriptive Analysis ◽  
Careful Analysis ◽  
Great Opportunity ◽  
Italian Association ◽  
Number Of Patients

UNSTRUCTURED Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

Sign in / Sign up

Export Citation Format

Share Document