Humanizing Artificial Intelligence

Israni, Sonoo Thadaney; Verghese, Abraham

doi:10.1001/jama.2018.19398

Cited by 75 publications

(52 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A rtificial intelligence (AI) holds immense promise for health care. [1][2][3] The field is evolving rapidly owing to increased computing capacity, availability of data and the widespread adoption of electronic health records in hospitals. However, current trends in big data use have brought about ethical concerns regarding accountability, responsibility and trust, among others.…”

mentioning

confidence: 99%

Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study

McCradden

Baba

Saha³

et al. 2020

cmajo

View full text Add to dashboard Cite

Background:As artificial intelligence (AI) approaches in research increase and AI becomes more integrated into medicine, there is a need to understand perspectives from members of the Canadian public and medical community. The aim of this project was to investigate current perspectives on ethical issues surrounding AI in health care. Methods:In this qualitative study, adult patients with meningioma and their caregivers were recruited consecutively (August 2018-February 2019) from a neurosurgical clinic in Toronto. Health care providers caring for these patients were recruited through snowball sampling. Based on a nonsystematic literature search, we constructed 3 vignettes that sought participants' views on hypothetical issues surrounding potential AI applications in health care. The vignettes were presented to participants in interviews, which lasted 15-45 minutes. Responses were transcribed and coded for concepts, frequency of response types and larger concepts emerging from the interview. Results:We interviewed 30 participants: 18 patients, 7 caregivers and 5 health care providers. For each question, a variable number of responses were recorded. The majority of participants endorsed nonconsented use of health data but advocated for disclosure and transparency. Few patients and caregivers felt that allocation of health resources should be done via computerized output, and a majority stated that it was inappropriate to delegate such decisions to a computer. Almost all participants felt that selling health data should be prohibited, and a minority stated that less privacy is acceptable for the goal of improving health. Certain caveats were identified, including the desire for deidentification of data and use within trusted institutions.Interpretation: In this preliminary study, patients and caregivers reported a mixture of hopefulness and concern around the use of AI in health care research, whereas providers were generally more skeptical. These findings provide a point of departure for institutions adopting health AI solutions to consider the ethical implications of this work by understanding stakeholders' perspectives. Abstract Research OPEN CMAJ OPEN, 8(1) E91 Affiliations: Division of Neurosurgery (McCradden,

show abstract

mentioning

confidence: 99%

Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study

McCradden

Baba

Saha³

et al. 2020

cmajo

View full text Add to dashboard Cite

show abstract

“…In addition, it is necessary now to make the language processing based on contents of knowledge, and it is also essential to have smart translation processing to gain knowledge and to deal with information naturally. In this case, future NLP systems will be smart and clever enough to express ideas and information and to explain news and knowledge contents like a natural human being [22]- [23].…”

Section: Resultsmentioning

confidence: 99%

Apps-based Machine Translation on Smart Media Devices - A Review

Gunarto

2019

Indonesian J. Comput. Cybern. Syst.

View full text Add to dashboard Cite

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists.

show abstract

“…These are merely hypothesis generators for more rigorous clinical studies/trials. 62,63 Therefore, clinical judgment, context, and rationale should be of paramount importance in the interpretation of studies using AI. There are also concerns about biased sampling in electronic health care (EHR) records related data studies as EHR oversample sicker individuals and individuals with health care access.…”

Section: Ai: Is It the Future? Or What Is The Future Of Ai?mentioning

confidence: 99%

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Mathur

Srivastava

et al. 2020

Clin Med Insights Cardiol

View full text Add to dashboard Cite

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

show abstract

Humanizing Artificial Intelligence

Cited by 75 publications

References 6 publications

Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study

Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study

Apps-based Machine Translation on Smart Media Devices - A Review

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Contact Info

Product

Resources

About