Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

Kalmady, Sunil V.; Greiner, Russell; Agrawal, Rimjhim; Shivakumar, Venkataram; Narayanaswamy, Janardhanan C.; Brown, Matthew; Greenshaw, Andrew J.; Dursun, Serdar; Venkatasubramanian, Ganesan

doi:10.1038/s41537-018-0070-8

Cited by 84 publications

(57 citation statements)

References 95 publications

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“…Many factors are encouraging the development of AI-Health (Chin-Yee & Upshur, 2019). One of the main driving forces is the belief that algorithms can make more objective, robust and evidence-based clinical decisions (in terms of diagnosis, prognosis or treatment recommendations) than a human health care provider (HCP) can (Kalmady et al, 2019). This is not an unfounded position.…”

Section: Epistemic Concerns: Inconclusive Inscrutable and Misguidedmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley¹

2019

Preprint

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Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

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Section: Epistemic Concerns: Inconclusive Inscrutable and Misguidedmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley¹

2019

Preprint

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“…AI plays a positive role in this process in the research of both Canadian and Indian scientists. They proposed an innovative model using AI to study the human brain, determine the human mental state and mental illness [28]. Australian scientists propose their approach to the development of artificial neural networks, assigning the functions of imitating the structural, functional and biological features of human neural networks to them [29].…”

Section: B Modern Approaches Using Artificial Intelligence In Identimentioning

confidence: 99%

Using Natural and Artificial Intelligence in the Talent Management System

Vinichenko*¹,

Rybakova²,

Сhulanova³

et al. 2019

IJRTE

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Abstract: The article describes the nature of the use of natural and artificial intelligence in the talent management system in the Moscow Region. The extent of using talent management technology, the tools, the ratio of demand for natural and artificial intelligence in talent management, and the level of employee confidence in man (robot) in management are analyzed. It is revealed and substantiated that personnel management services and the management of companies do not effectively use natural intelligence in the framework of the talent management system, relying on chance and a typical approach. Ambiguity in the assessment of artificial intelligence by both employees and company management was revealed. Increased use and fears of the negative impact of artificial intelligence become opposing factors in talent management. It is proposed to quickly implement advanced technologies based on artificial intelligence, with no harm to the human and his potential. It is also proposed to constantly keep in focus the risks of intercepting initiatives in the management of artificial intelligence and the crowding out of a person from the labor market. The main advantage of the article is an integrated approach to the study of the pattern of using natural and artificial intelligence in the talent management system. The authors considered the issue of identifying the ratio of artificial and natural intelligence in talent management for the first time. The results can serve as a basis for further research in the system of human resource management, as well as good support for making management decisions in the implementation of artificial intelligence in business processes and organization management.

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“…The talents go head to head with artificial intelligence produced by them. It has impact on the mental state of a person [33], [34].…”

Section: Developmentmentioning

confidence: 99%

Fostering Talented Employees' Loyalty in Circumstances Where Artificial Intelligence is Applied

Vinichenko*¹,

Сhulanova²,

Rybakova³

et al. 2019

IJRTE

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 Abstract: The article discusses the problem of fostering talented employees' loyalty in circumstances where artificial intelligence is applied. The intensification of competition under the conditions of another forthcoming economic crisis triggered the technological application of artificial intelligence with the conjoined fostering of talented employees' loyalty. The analysis of the results of the Google Form survey by the focus group has shown that a favorable environment prevails within organizations, which in general allows using talented employees' potential efficiently to the maximum possible extent. Discrimination, envy, and indifference of colleagues do not have that much effect on talented employees' loyalty. Creation of more favorable labor conditions for talented employees is contradictory and predetermines the discontent of other corporate employees, which may result in conflicts, the ethnic ones inclusive. The article presents the nature of the influence of artificial intelligence on talented employees' development for the first time. The majority of respondents regard this effect as a positive one (63%). The originality of the research lies in revealing the character of relations between the leaders of natural intelligencetalented employeesand artificial intelligence. This study may form the basis for reducing risks while working with talented employees in conditions where artificial intelligence is applied.

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Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

Cited by 84 publications

References 95 publications

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Using Natural and Artificial Intelligence in the Talent Management System

Fostering Talented Employees' Loyalty in Circumstances Where Artificial Intelligence is Applied

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