scholarly journals Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

2019 ◽  
Vol 70 (2) ◽  
pp. 107-118 ◽  
Author(s):  
◽  
Jacob L. Jaremko ◽  
Marleine Azar ◽  
Rebecca Bromwich ◽  
Andrea Lum ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Privacy ◽  
Ethical Issues ◽  
Expert Knowledge ◽  
Disease Diagnosis ◽  
White Paper ◽  
Imaging Features ◽  
Learning Approaches ◽  
Treatment Optimization ◽  
Canadian Association

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Artificial Intelligence (AI) Ethics

2020 ◽  
Vol 31 (2) ◽  
pp. 74-87 ◽  
Author(s):  
Keng Siau ◽  
Weiyu Wang
Keyword(s):  
Artificial Intelligence ◽  
Data Privacy ◽  
Ethical Issues ◽  
Well Being ◽  
Critical Issue ◽  
Ethical Principles ◽  
Machine Ethics ◽  
Moral Issues ◽  
Ai Ethics ◽  
Self Driving Cars

Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept of “machine ethics” was proposed around 2006, AI ethics is still in the infancy stage. AI ethics is the field related to the study of ethical issues in AI. To address AI ethics, one needs to consider the ethics of AI and how to build ethical AI. Ethics of AI studies the ethical principles, rules, guidelines, policies, and regulations that are related to AI. Ethical AI is an AI that performs and behaves ethically. One must recognize and understand the potential ethical and moral issues that may be caused by AI to formulate the necessary ethical principles, rules, guidelines, policies, and regulations for AI (i.e., Ethics of AI). With the appropriate ethics of AI, one can then build AI that exhibits ethical behavior (i.e., Ethical AI). This paper will discuss AI ethics by looking at the ethics of AI and ethical AI. What are the perceived ethical and moral issues with AI? What are the general and common ethical principles, rules, guidelines, policies, and regulations that can resolve or at least attenuate these ethical and moral issues with AI? What are some of the necessary features and characteristics of an ethical AI? How to adhere to the ethics of AI to build ethical AI?

scholarly journals Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

2018 ◽  
Vol 69 (2) ◽  
pp. 120-135 ◽  
Author(s):  
An Tang ◽  
Roger Tam ◽  
Alexandre Cadrin-Chênevert ◽  
Will Guest ◽  
Jaron Chong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Care ◽  
Working Group ◽  
White Paper ◽  
Patient Centered ◽  
Computing Power ◽  
Implementation Phase ◽  
Early Adoption ◽  
Canadian Association

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

scholarly journals Artificial Intelligence in Health Informatics: Hype or Reality?

2019 ◽  
Vol 28 (01) ◽  
pp. 003-004 ◽  
Author(s):  
Kate Fultz Hollis ◽  
Lina F. Soualmia ◽  
Brigitte Séroussi
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Expert Knowledge ◽  
Improve Patient Care ◽  
Health Data ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Data Driven ◽  
Editorial Team ◽  
Special Topic

Objectives: To provide an introduction to the 2019 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial presents an overview and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The special topic is discussed, the IMIA President’s statement is introduced, and changes in the Yearbook editorial team are described. Results: Artificial intelligence (AI) in Medicine arose in the 1970’s from new approaches for representing expert knowledge with computers. Since then, AI in medicine has gradually evolved toward essentially data-driven approaches with great results in image analysis. However, data integration, storage, and management still present clear challenges among which the lack of explanability of the results produced by data-driven AI methods. Conclusion: With more health data availability, and the recent developments of efficient and improved machine learning algorithms, there is a renewed interest for AI in medicine.The objective is to help health professionals improve patient care while also reduce costs. However, the other costs of AI, including ethical issues when processing personal health data by algorithms, should be included.

scholarly journals Supervised Phenotype Discovery from Multimodal Brain Imaging

2021 ◽  
Author(s):  
Weikang Gong ◽  
Song Bai ◽  
Ying-Qiu Zheng ◽  
Stephen Smith ◽  
Christian Beckmann
Keyword(s):  
Brain Imaging ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Expert Knowledge ◽  
Parameter Tuning ◽  
Imaging Features ◽  
Learning Approaches ◽  
Imaging Data ◽  
Computationally Efficient ◽  
Multiple Imaging

Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects' demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without use of information in nIDPs. In this paper, we proposed Supervised BigFLICA (SuperBigFLICA), a semi-supervised, multimodal, and multi-task fusion approach for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely bypasses the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension scores.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

The Oxford Handbook of Ethics of AI

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Rapid Development ◽  
Data Driven ◽  
International Human Rights Law ◽  
Human Knowledge ◽  
Human Rights Law ◽  
Ai Ethics ◽  
And Behavior ◽  
Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

Ethics of Artificial Intelligence in Transport

2020 ◽  
pp. 667-683
Author(s):  
Bryant Walker Smith
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Ethical Question ◽  
Power Dynamics ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The U.S ◽  
Policy Solutions

This chapter highlights key ethical issues in the use of artificial intelligence in transport by using automated driving as an example. These issues include the tension between technological solutions and policy solutions; the consequences of safety expectations; the complex choice between human authority and computer authority; and power dynamics among individuals, governments, and companies. In 2017 and 2018, the U.S. Congress considered automated driving legislation that was generally supported by many of the larger automated-driving developers. However, this automated-driving legislation failed to pass because of a lack of trust in technologies and institutions. Trustworthiness is much more of an ethical question. Automated vehicles will not be driven by individuals or even by computers; they will be driven by companies acting through their human and machine agents. An essential issue for this field—and for artificial intelligence generally—is how the companies that develop and deploy these technologies should earn people’s trust.

scholarly journals Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
J. Raymond Geis ◽  
Adrian Brady ◽  
Carol C. Wu ◽  
Jack Spencer ◽  
Erik Ranschaert ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
European Society ◽  
Common Good ◽  
Autonomous Systems ◽  
Codes Of Ethics ◽  
Well Being ◽  
Imaging Informatics ◽  
Financial Gain ◽  
Canadian Association ◽  
Ultimate Responsibility

Abstract This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

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