Will artificial intelligence solve the human resource crisis in healthcare?

Meskó, Bertalan; Hetényi, Gergely; Győrffy, Zsuzsanna

doi:10.1186/s12913-018-3359-4

Cited by 223 publications

(168 citation statements)

References 8 publications

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“…The authors conclude that their study is close to other research's happened in this area. However, authors believe with more data, the results will be more accurate [5].…”

Section: Related Workmentioning

confidence: 90%

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Work-Life Balance Analysis Score Model

Prasad,

Saradhi

2019

IJEAT

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Most of the companies are finding innovative ways to provide work-life balance to employees. Some of the measures creches for their children, flexible work timings, paternity leaves among others. Some of the companies are looking at technology to provide a better work-life balance. With the increasing need for a more integrated model of analyzing the work-life balance, in this manuscript, a contemporary model of machine learning-based work-life balance score analysis system is proposed, which indicates potential performance over the training and test pattern used for analysis. Though the scope for improving the accuracy of the system exists, still in terms of the pragmatic application of the model, it can be stated that the model is effective and has a scope of implementation over the real-time conditions.

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“…The authors conclude that their study is close to other research's happened in this area. However, authors believe with more data, the results will be more accurate [5].…”

Section: Related Workmentioning

confidence: 90%

“…In [5], the authors focus to understand, and the study has spoken and unspoken emotions. The authors state that many researchers have worked on understanding emotions and how they can be detected through perception and various theories on psychology.…”

Section: Related Workmentioning

confidence: 99%

Work-Life Balance Analysis Score Model

Prasad,

Saradhi

2019

IJEAT

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“…Based on the number of articles, we may wonder to what extent this is another case of temporary hype or if there are substantial clinical applications beyond the hype [7]. In addition to the high expectations regarding the impacts of AI on knowledge work [8], the fear of change and losing jobs is also salient [9].…”

Section: Introductionmentioning

confidence: 99%

Promises of artificial intelligence in neuroradiology: a systematic technographic review

2020

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Purpose To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. Methods We identified AI applications offered on the market during the period 2017–2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of ‘supporting’, ‘extending’ and ‘replacing’ radiology tasks. Results We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities ‘support’ radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities ‘extends’ the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to ‘replace’ certain radiology tasks. Conclusion Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

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“…One of the difficult steps in establishing the AI-based diagnostic process is feedbacking the achieved precision of AI-based diagnosis to the initially built diagnostic algorithm. AI reinforced by ML techniques is expected to help us partly overcome this problem (Mesko et al 2018). Such diagnostic technology will be developed and progressed through the collaboration of clinicians and information technology companies in the near future.…”

Section: Introductionmentioning

confidence: 99%

Application of Large Electronic Medical Database for Detecting Undiagnosed Patients in the General Population

Ishii

Akaishi

Fujimori

et al. 2019

Tohoku J. Exp. Med.

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Clinical application of accumulated medical big data is a hot topic in medical informatics. Not only for suggesting possible diagnoses in each individual, large medical database can be possibly used for detecting undiagnosed patients in the general population. In this study, we tried to develop a computerized system of detecting overlooked undiagnosed patients with rare chronic diseases in the community population by utilizing the uniformed national medical insurance record database. A cumulative total of 489,823 hospital visits at one tertiary medical center were collected for this project. As the target disease, we selected esophagogastric junction outflow obstruction (EGJOO), including achalasia, which is known to be easily overlooked without performing a barium swallow test. Patient selection software automatically picked out 17,814 individuals with the given suspected diagnoses that could be misdiagnosed in patients with the target disease, from which the software further picked out 526 individuals who underwent upper endoscopy but did not undergo barium swallow test. Of them, the hospital medical records suggested that 39 people still suffered from prolonged symptoms lasting for more than 6 months after the first hospital visit. Among them, 16 individuals agreed to undergo the barium swallow test. One of them was confirmed to suffer from EGJOO, possibly based on some undiagnosed connective tissue diseases. An automated computerized detection system with uniform big medical data would realize more efficient and less expensive screening system for undiagnosed chronic diseases in the general population based on symptoms and previously performed examinations in each individual.

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Will artificial intelligence solve the human resource crisis in healthcare?

Cited by 223 publications

References 8 publications

Work-Life Balance Analysis Score Model

Work-Life Balance Analysis Score Model

Promises of artificial intelligence in neuroradiology: a systematic technographic review

Application of Large Electronic Medical Database for Detecting Undiagnosed Patients in the General Population

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