scholarly journals Interim evaluation of Google AI forecasting for COVID-19 compared with statistical forecasting by human intelligence in the first week

2020 ◽  
Author(s):  
Junko Kurita ◽  
Tamie Sugawara ◽  
Yasushi Ohkusa
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Statistical Model ◽  
Onset Date ◽  
Human Intelligence ◽  
Epidemic Curve ◽  
Number Of Patients ◽  
The Usa ◽  
Discrepancy Rate ◽  
Traditional Surveillance

AbstractBackgroundSince June, Google (Alphabet Inc.) has provided forecasting for COVID-19 outbreak by artificial intelligence (AI) in the USA. In Japan, they provided similar services from November, 2020.ObjectWe compared Google AI forecasting with a statistical model by human intelligence.MethodWe regressed the number of patients whose onset date was day t on the number of patients whose past onset date was 14 days prior, with information about traditional surveillance data for common pediatric infectious diseases including influenza, and prescription surveillance 7 days prior. We predicted the number of onset patients for 7 days, prospectively. Finally, we compared the result with Googles AI-produced forecast. We used the discrepancy rate to evaluate the precision of prediction: the sum of absolute differences between data and prediction divided by the aggregate of data.ResultsWe found Google prediction significantly negative correlated with the actual observed data, but our model slightly correlated but not significant. Moreover, discrepancy rate of Google prediction was 27.7% for the first week. The discrepancy rate of our model was only 3.47%.Discussion and ConclusionResults show Googles prediction has negatively correlated and greater difference with the data than our results. Nevertheless, it is noteworthy that this result is tentative: the epidemic curve showing newly onset patients was not fixed.

Intelligence, Artificial and Otherwise

2019 ◽  
Vol 24 (2) ◽  
pp. 241-258
Author(s):  
Paul Dumouchel
Keyword(s):  
Artificial Intelligence ◽  
Human Intelligence

The idea of artificial intelligence implies the existence of a form of intelligence that is “natural,” or at least not artificial. The problem is that intelligence, whether “natural” or “artificial,” is not well defined: it is hard to say what, exactly, is or constitutes intelligence. This difficulty makes it impossible to measure human intelligence against artificial intelligence on a unique scale. It does not, however, prevent us from comparing them; rather, it changes the sense and meaning of such comparisons. Comparing artificial intelligence with human intelligence could allow us to understand both forms better. This paper thus aims to compare and distinguish these two forms of intelligence, focusing on three issues: forms of embodiment, autonomy and judgment. Doing so, I argue, should enable us to have a better view of the promises and limitations of present-day artificial intelligence, along with its benefits and dangers and the place we should make for it in our culture and society.

scholarly journals Rapid implementation of Inpatient eConsult Programme addresses new challenges for patient care during COVID-19 pandemic

2021 ◽  
pp. bmjinnov-2020-000557
Author(s):  
Sharon Rikin ◽  
Eric J Epstein ◽  
Inessa Gendlina
Keyword(s):  
Palliative Care ◽  
Infectious Diseases ◽  
Patient Care ◽  
Personal Protective Equipment ◽  
Protective Equipment ◽  
Qualitative Feedback ◽  
The Usa ◽  
New Challenges ◽  
Science Framework ◽  
Context Specific

IntroductionAt the early epicentre of the COVID-19 crisis in the USA, our institution saw a surge in the demand for inpatient consultations for areas impacted by COVID-19 (eg, infectious diseases, nephrology, palliative care) and shortages in personal protective equipment (PPE). We aimed to provide timely specialist input for consult requests during the COVID-19 pandemic by implementing an Inpatient eConsult Programme.MethodsWe used the reach, effectiveness, adoption, implementation and maintenance implementation science framework and run chart analysis to evaluate the reach, adoption and maintenance of the Inpatient eConsult Programme compared with traditional in-person consults. We solicited qualitative feedback from frontline physicians and specialists for programme improvements.ResultsDuring the study period, there were 46 available in-person consult orders and 21 new eConsult orders. At the peak of utilisation, 42% of all consult requests were eConsults, and by the end of the study period, utilisation fell to 20%. Qualitative feedback revealed subspecialties best suited for eConsults (infectious diseases, nephrology, haematology, endocrinology) and influenced improvements to the ordering workflow, documentation, billing and education regarding use.DiscussionWhen offered inpatient eConsult requests as an alternative to in-person consults in the context of a surge in patients with COVID-19, frontline physicians used eConsult requests and decreased use of in-person consults. As the demand for consults decreased and PPE shortages were no longer a major concern, eConsult utilisation decreased, revealing a preference for in-person consultations when possible.ConclusionsLessons learnt can be used to develop and implement inpatient eConsults to meet context-specific challenges at other institutions.

scholarly journals Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 441
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Li-Fong Lin ◽  
Shuo-Chen Chien ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
English Language ◽  
R Package ◽  
Research Area ◽  
Republic Of China ◽  
Comprehensive Picture ◽  
The Usa ◽  
The Republic ◽  
Academy Of Sciences

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

scholarly journals Machine Learning in Healthcare Communication

Encyclopedia ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 220-239
Author(s):  
Sarkar Siddique ◽  
James C. L. Chow
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Therapy ◽  
Medical Imaging ◽  
Health Education ◽  
Human Intelligence ◽  
Computer Algorithms ◽  
Dialogue Management ◽  
Topical Review ◽  
Healthcare Communication

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

scholarly journals Artificial Intelligence, Smart Contract and Islamic Finance

2018 ◽  
Vol 14 (2) ◽  
pp. 145 ◽  
Author(s):  
Siti Rohaya Mat Rahim ◽  
Zam Zuriyati Mohamad ◽  
Juliana Abu Bakar ◽  
Farhana Hanim Mohsin ◽  
Norhayati Md Isa
Keyword(s):  
Artificial Intelligence ◽  
Computer Code ◽  
Islamic Finance ◽  
Human Intelligence ◽  
Human Beings ◽  
Finance Industry ◽  
Smart Contract ◽  
Huge Impact ◽  
Block Chain

This study examines the two important aspect of latest technology issues in Islamic finance that related to artificial intelligence (AI) and smart contract. AI refers to the ability of machines to understand, think, and learn in a similar way to human beings, indicating the possibility of using computers to simulate human intelligence. Smart contract is a computer code running on top of a block-chain containing a set of rules under which the parties to that smart contract agree to interact with each other. The main objectives of this article are to evaluate the operations of AI and smart contract, to make comparison between the operations of AI and smart contract. This article concludes that AI and smart contract will have a huge impact in future for Islamic Finance industry.

scholarly journals Infectious diseases acquired by international travellers visiting the USA†

2018 ◽  
Vol 25 (1) ◽  
Author(s):  
Rhett J Stoney ◽  
Douglas H Esposito ◽  
Phyllis Kozarsky ◽  
Davidson H Hamer ◽  
Martin P Grobusch ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Descriptive Analysis ◽  
Low Frequency ◽  
Developed Countries ◽  
Middle Income ◽  
Middle Income Countries ◽  
International Tourist ◽  
Clinical Diagnoses ◽  
The Usa ◽  
Systemic Infections

Abstract Background Estimates of travel-related illness have focused predominantly on populations from highly developed countries visiting low- or middle-income countries, yet travel to and within high-income countries is very frequent. Despite being a top international tourist destination, few sources describe the spectrum of infectious diseases acquired among travellers to the USA. Methods We performed a descriptive analysis summarizing demographic and travel characteristics, and clinical diagnoses among non-US-resident international travellers seen during or after travel to the USA at a GeoSentinel clinic from 1 January 1997 through 31 December 2016. Results There were 1222 ill non-US-resident travellers with 1393 diagnoses recorded during the 20-year analysis period. Median age was 40 (range 0–86 years); 52% were female. Patients visited from 63 countries and territories, most commonly Canada (31%), Germany (14%), France (9%) and Japan (7%). Travellers presented with a range of illnesses; skin and soft tissue infections of unspecified aetiology were the most frequently reported during travel (29 diagnoses, 14% of during-travel diagnoses); arthropod bite/sting was the most frequently reported after travel (173 diagnoses, 15% after-travel diagnoses). Lyme disease was the most frequently reported arthropod-borne disease after travel (42, 4%). Nonspecific respiratory, gastrointestinal and systemic infections were also among the most frequently reported diagnoses overall. Low-frequency illnesses (<2% of cases) made up over half of diagnoses during travel and 41% of diagnoses after travel, including 13 cases of coccidioidomycosis and mosquito-borne infections like West Nile, dengue and Zika virus diseases. Conclusions International travellers to the USA acquired a diverse array of mostly cosmopolitan infectious diseases, including nonspecific respiratory, gastrointestinal, dermatologic and systemic infections comparable to what has been reported among travellers to low- and middle-income countries. Clinicians should consider the specific health risks when preparing visitors to the USA and when evaluating and treating those who become ill.

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