Editorial Comment: Real-World Imaging Artificial Intelligence Considerations for COVID-19 and Beyond

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

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A Survey on Bias and Fairness in Machine Learning

ACM Computing Surveys ◽

10.1145/3457607 ◽

2021 ◽

Vol 54 (6) ◽

pp. 1-35

Author(s):

Ninareh Mehrabi ◽

Fred Morstatter ◽

Nripsuta Saxena ◽

Kristina Lerman ◽

Aram Galstyan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Real World ◽

State Of The Art ◽

Future Directions ◽

Discriminatory Behavior ◽

Real World Applications ◽

Near Future ◽

Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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Real-world analysis of artificial intelligence in musculoskeletal trauma

Journal of Clinical Orthopaedics and Trauma ◽

10.1016/j.jcot.2021.101573 ◽

2021 ◽

Vol 22 ◽

pp. 101573

Author(s):

Pranav Ajmera ◽

Amit Kharat ◽

Rajesh Botchu ◽

Harun Gupta ◽

Viraj Kulkarni

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Musculoskeletal Trauma

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Deep learning-enabled medical computer vision

npj Digital Medicine ◽

10.1038/s41746-020-00376-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Andre Esteva ◽

Katherine Chou ◽

Serena Yeung ◽

Nikhil Naik ◽

Ali Madani ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Medical Imaging ◽

Real World ◽

Recent Progress ◽

Medical Applications ◽

Modern Computer ◽

Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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Against generalisation: Data-driven decisions need context to be human-compatible

Business Information Review ◽

10.1177/02663821211061986 ◽

2021 ◽

pp. 026638212110619

Author(s):

Sharon Richardson

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Data Science ◽

Machine Learning Algorithms ◽

Data Driven ◽

Theoretical Frameworks ◽

The Past ◽

Real World Applications ◽

Contextual Framework ◽

Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

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Artificial Intelligence (AI) and Its Associated Ethical Issues

ICR Journal ◽

10.52282/icr.v10i1.78 ◽

2019 ◽

Vol 10 (1) ◽

pp. 124-126

Author(s):

Shahino Mah Abdulllah

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Ethical Issues ◽

Economic Inequality ◽

Human Behaviour ◽

Intelligent Machines ◽

Normal Shift

The adoption of AI in many industries has been regarded by some as a threat to low- and middle-skill workers, as it will drastically cut down reliance on the human workforce. Besides unemployment, there are also concerns about rising economic inequality caused by AI-driven companies. With fewer employees, these companies could gain a disproportionate advantage over conventional companies that still depend on normal, shift-based systems. There is also the issue that some AI bots have achieved the capability to interact with humans and build relationships through conversations. This influential communication could eventually enable these bots to affect human behaviour and possibly trigger certain actions. Significantly, therefore, such intelligent machines are not immune to mistakes and confusion since not all possible examples of real world interaction are covered during their training; this weakness could be manipulated to fulfil certain ends. Also, Al cannot be expected to be entirely fair and neutral, since it is dependent on human programmers, who have their own interests and whims.

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Real World of Artificial Intelligence - A Review

Journal of Technology Management for Growing Economies ◽

10.15415/jtmge.2020.112005 ◽

2020 ◽

Vol 11 (2) ◽

pp. 41-47

Author(s):

Amandeep Kaur ◽

Madhu Dhiman ◽

Mansi Tonk ◽

Ramneet Kaur

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Natural Language Processing ◽

Face Recognition ◽

Language Processing ◽

Real World ◽

Review Paper ◽

General Concept ◽

Research Trends ◽

Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

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Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

10.24963/kr.2021/43 ◽

2021 ◽

Author(s):

Ruiqi Li ◽

Hua Hua ◽

Patrik Haslum ◽

Jochen Renz

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Spatial Relations ◽

State Transitions ◽

Conceptual Similarity ◽

Physical Environments ◽

The World ◽

Unsupervised Approach ◽

Action Models ◽

Unseen Objects

Detecting, characterizing and adapting to novelty, whether in the form of previously unseen objects or phenomena, or unexpected changes in the behavior of known elements, is essential for Artificial Intelligence agents to operate reliably in unconstrained real-world environments. We propose an automatic, unsupervised approach to novelty characterization for dynamic domains, based on describing the behaviors and interactions of objects in terms of their possible actions. To abstract from the variety of realizations of an action that can occur in physical domains, we model states in terms of qualitative spatial relations (QSRs) between their entities. By first learning a model of actions in the non-novel environment from the state transitions observed as the agent interacts with the world, we can detect novelty by the persistent deviations from this model that it causes, and characterize the novelty by new or modified actions. We also present a new method of learning action models from observation, based on conceptual similarity and hierarchical clustering.

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Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis

10.1101/2021.10.05.21264578 ◽

2021 ◽

Author(s):

Gregory M Miller ◽

Austin J Ellis ◽

Rangaprasad Sarangarajan ◽

Amay Parikh ◽

Leonardo O Rodrigues ◽

...

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Clinical Decision Making ◽

Clinical Decision ◽

Long Term Effects ◽

Real World Data ◽

World Data ◽

Antiemetic Agent ◽

Future Loss ◽

Selection Operator

Objective: The COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. Materials and Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. Conclusions: The results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.

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