A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi, Tharindu; Reis, Andrew; Nanayakkara, Suranga

doi:10.3390/s21072514

Cited by 71 publications

(40 citation statements)

References 212 publications

(356 reference statements)

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“…The term artificial intelligence (AI) was coined during the 1940s [ 7 ]. Deep learning is a form of machine learning which uses convolutional neuronal networks to solve both simple and complex tasks [ 8 ]. In radiology, AI is increasingly perceived as an opportunity to optimize medical care [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

et al. 2021

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Background Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services. Methods In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors. Results 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results. Conclusion Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT. Trial registration German Clinical Trials Register (DRKS-ID: DRKS00023593).

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Section: Introductionmentioning

confidence: 99%

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

et al. 2021

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“…ML algorithms have been used together with RNA-Seq expression data to identify genes associated with feed efficiency in pigs, and to classify animals' phenotypic extreme for residual feed intake [244]. Artificial Intelligence (AI) uses algorithms that automate the decision process [245], while Machine Learning (ML) uses AI to automatically learn complex relationships and patterns in data [246,247]. ML algorithms may be unsupervised or supervised.…”

Section: Artificial Intelligence and Machine Learning Approachesmentioning

confidence: 99%

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

Passamonti

Somenzi

Barbato

et al. 2021

Animals

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Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.

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“…The tension between machine autonomy and human control gave rise recently to a field called Human Centered Machine Learning, which focuses on the development of HCAI systesm that adopt models that can be trained via machine learning techniques. See Kaluarachchi et al [7] for a review of the recent literature. The main objective here is to develop AI models that take into account the input provided by humans when they get to a decision.…”

Section: Related Workmentioning

confidence: 99%

On some Foundational Aspects of Human-Centered Artificial Intelligence

Serafini¹,

Barbosa²,

Grosinger

et al. 2021

Preprint

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The burgeoning of AI has prompted recommendations that AI techniques should be "human-centered". However, there is no clear definition of what is meant by Human Centered Artificial Intelligence, or for short, HCAI. This paper aims to improve this situation by addressing some foundational aspects of HCAI. To do so, we introduce the term HCAI agent to refer to any physical or software computational agent equipped with AI components and that interacts and/or collaborates with humans. This article identifies five main conceptual components that participate in an HCAI agent: Observations, Requirements, Actions, Explanations and Models. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI. In this paper, we focus our analysis on scenarios consisting of a single agent operating in dynamic environments in presence of humans.

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Cited by 71 publications

References 212 publications

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

On some Foundational Aspects of Human-Centered Artificial Intelligence

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