Artificial intelligence in computational pathology – challenges and future directions

2021 ◽  
pp. 103196
Author(s):  
Sandra Morales ◽  
Kjersti Engan ◽  
Valery Naranjo
Keyword(s):  
Artificial Intelligence ◽  
Future Directions ◽  
Computational Pathology

scholarly journals Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Cultural Heritage ◽  
Conceptual Framework ◽  
Interaction Design ◽  
Participatory Design ◽  
Future Directions ◽  
Heritage Sites ◽  
Review Analysis

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

scholarly journals A Survey on Bias and Fairness in Machine Learning

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.

scholarly journals Applications of Robotics, AI, and Digital Technologies During COVID-19: A Review

2021 ◽  
pp. 1-23
Author(s):  
Zhuo Zhao ◽  
Yangmyung Ma ◽  
Adeel Mushtaq ◽  
Abdul M. Azam Rajper ◽  
Mahmoud Shehab ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Technology Adoption ◽  
Digital Technology ◽  
Digital Technologies ◽  
Rapid Diagnosis ◽  
Quick Response ◽  
Virus Spread ◽  
Future Directions ◽  
Public Areas ◽  
Potential Impact

Abstract Many countries have enacted a quick response to the unexpected COVID-19 pandemic by utilizing existing technologies. For example, robotics, artificial intelligence, and digital technology have been deployed in hospitals and public areas for maintaining social distancing, reducing person-to-person contact, enabling rapid diagnosis, tracking virus spread, and providing sanitation. In this paper, 163 news articles and scientific reports on COVID-19-related technology adoption were screened, shortlisted, categorized by application scenario, and reviewed for functionality. Technologies related to robots, artificial intelligence, and digital technology were selected from the pool of candidates, yielding a total of 50 applications for review. Each case was analyzed for its engineering characteristics and potential impact on the COVID-19 pandemic. Finally, challenges and future directions regarding the response to this pandemic and future pandemics were summarized and discussed.

A Critical Assessment and Review of Artificial Intelligence in Facial Paralysis Analysis: Uncovering the Truth

FACE ◽  
2021 ◽  
pp. 273250162110228
Author(s):  
David T. Mitchell ◽  
David Z. Allen ◽  
Matthew R. Greives ◽  
Phuong D. Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Facial Paralysis ◽  
Critical Assessment ◽  
Facial Emotion ◽  
Data Systems ◽  
Computer Algorithms ◽  
Future Directions ◽  
Sample Data ◽  
Before And After ◽  
Facial Reanimation

Machine learning is a rapidly growing subset of artificial intelligence (AI) which involves computer algorithms that automatically build mathematical models based on sample data. Systems can be taught to learn from patterns in existing data in order to make similar conclusions from new data. The use of AI in facial emotion recognition (FER) has become an area of increasing interest for providers who wish to quantify facial emotion before and after interventions such as facial reanimation surgery. While FER deep learning algorithms are less subjective when compared to layperson assessments, the databases used to train them can greatly alter their outputs. There are currently many well-established modalities for assessing facial paralysis, but there is also increasing interest in a more objective and universal measurement system to allow for consistent assessments between practitioners. The purpose of this article is to review the development of AI, examine its existing uses in facial paralysis assessment, and discuss the future directions of its implications.

scholarly journals Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

2019 ◽  
Vol 4 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Omer F Ahmad ◽  
Antonio S Soares ◽  
Evangelos Mazomenos ◽  
Patrick Brandao ◽  
Roser Vega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Diagnosis ◽  
Current Evidence ◽  
Future Directions ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Ethical issues on artificial intelligence in radiology: how is it reported in research articles? The current state and future directions

2021 ◽  
Vol 90 (2) ◽  
pp. e513
Author(s):  
Tomasz Piotrowski ◽  
Joanna Kazmierska ◽  
Mirosława Mocydlarz-Adamcewicz ◽  
Adam Ryczkowski
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Real Life ◽  
Exclusion Criteria ◽  
Future Directions ◽  
Current State ◽  
Ethical Status ◽  
The Status ◽  
Clinical Questions ◽  
Almost All

Background. This paper evaluates the status of reporting information related to the usage and ethical issues of artificial intelligence (AI) procedures in clinical trial (CT) papers focussed on radiology issues as well as other (non-trial) original radiology articles (OA). Material and Methods. The evaluation was performed by three independent observers who were, respectively physicist, physician and computer scientist. The analysis was performed for two groups of publications, i.e., for CT and OA. Each group included 30 papers published from 2018 to 2020, published before guidelines proposed by Liu et al. (Nat Med. 2020; 26:1364-1374). The set of items used to catalogue and to verify the ethical status of the AI reporting was developed using the above-mentioned guidelines. Results. Most of the reviewed studies, clearly stated their use of AI methods and more importantly, almost all tried to address relevant clinical questions. Although in most of the studies, patient inclusion and exclusion criteria were presented, the widespread lack of rigorous descriptions of the study design apart from a detailed explanation of the AI approach itself is noticeable. Few of the chosen studies provided information about anonymization of data and the process of secure data sharing. Only a few studies explore the patterns of incorrect predictions by the proposed AI tools and their possible reasons. Conclusion. Results of review support idea of implementation of uniform guidelines for designing and reporting studies with use of AI tools. Such guidelines help to design robust, transparent and reproducible tools for use in real life.

scholarly journals Trusted Partners: Human-Machine Teaming and the Future of Military AI

2021 ◽  
Author(s):  
Margarita Konaev ◽  
Tina Huang ◽  
Husanjot Chahal
Keyword(s):  
Artificial Intelligence ◽  
Future Directions ◽  
The Future ◽  
The U.S

As the U.S. military integrates artificial intelligence into its systems and missions, there are outstanding questions about the role of trust in human-machine teams. This report examines the drivers and effects of such trust, assesses the risks from too much or too little trust in intelligent technologies, reviews efforts to build trustworthy AI systems, and offers future directions for research on trust relevant to the U.S. military.

scholarly journals Ethical issues in computational pathology

2021 ◽  
pp. medethics-2020-107024
Author(s):  
Tom Sorell ◽  
Nasir Rajpoot ◽  
Clare Verrill
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Ethical Issues ◽  
Recent Literature ◽  
Personal Data ◽  
Ethical Problems ◽  
Data Ethics ◽  
Slide Image ◽  
Data Protection Law ◽  
Computational Pathology

This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from (1) the tension between artificial intelligence (AI) research—with its hunger for more and more data—and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; (2) the fact that computational pathology lends itself to kinds of data fusion that go against data ethics norms and some norms of biobanking; (3) the fact that AI methods are esoteric and produce results that are sometimes unexplainable (the so-called ‘black box’problem) and (4) the fact that computational pathology is particularly dependent on scanning technology manufacturers with interests of their own in profit-making from data collection. We shall suggest that most of these issues are resolvable.

scholarly journals The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Gulshan Kumar ◽  
Krishan Kumar
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Classification Performance ◽  
Intrusion Detection Systems ◽  
Future Directions ◽  
Comprehensive Review ◽  
Detection Systems ◽  
Combination Methods

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).

Sign in / Sign up

Export Citation Format

Share Document