Medical ethics considerations on artificial intelligence

Keskinbora, Hıdır Kadircan

doi:10.1016/j.jocn.2019.03.001

Cited by 184 publications

(137 citation statements)

References 13 publications

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“…Few data are available about the role of artificial intelligence as a radiological decision support system in COVID-19 patients; it is too early to apply this technology in clinical practice as first-line modality. There are in fact many ethical and medico-legal implications in the use of this technology that, even in this emergency situation, do not allow its routine use [23][24][25]. As for any new technology, a clinical validation phase is needed with single or multicenter trials, and standardized approaches for imaging methodology and artificial intelligence algorithms.…”

Section: Discussionmentioning

confidence: 99%

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

et al. 2020

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The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients.(1) SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. (2) SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. (3) Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

et al. 2020

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“…A recent perspective article provides an overview of the barriers to deployment and translational impact of ML methods for health care [7]. The operation and fitting of ML methods [8], the ethics of AI in medicine [9], as well as unintended consequences [10,11] are comprehensively discussed elsewhere.…”

Section: Pros and Cons Of ML Modelsmentioning

confidence: 99%

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Davidson

Boland

2020

J Pharmacokinet Pharmacodyn

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The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and SCOPUS. Search terms included pregnancy and AI. Research articles and book chapters were included, while conference papers, editorials and notes were excluded from the review. Included papers focused on pregnancy and AI methods, and pertained to pharmacologic interventions. We identified 376 distinct studies from our queries. A final set of 31 papers were included for the review. Included papers represented a variety of pregnancy concerns and multidisciplinary applications of AI. Few studies relate to pregnancy, AI, and pharmacologics and therefore, we review carefully those studies. External validation of models and techniques described in the studies is limited, impeding on generalizability of the studies. Our review describes how AI has been applied to address maternal health, throughout the pregnancy process: preconception, prenatal, perinatal, and postnatal health concerns. However, there is a lack of research applying AI methods to understand how pharmacologic treatments affect pregnancy. We identify three areas where AI methods could be used to improve our understanding of pharmacological effects of pregnancy, including: (a) obtaining sound and reliable data from clinical records (15 studies), (b) designing optimized animal experiments to validate specific hypotheses (1 study) to (c) implementing decision support systems that inform decision-making (11 studies). The largest literature gap that we identified is with regards to using AI methods to optimize translational studies between animals and humans for pregnancyrelated drug exposures.

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“…Moreover, there are privacy, anonymity, ethical, and medicolegal concerns for the adoption of AI-enabled systems in medical practices and research. 28,29 The machine performs the task according to the specific instructions given; however, it gradually learns to be flexible and to work in various situations through using newly acquired data. This triggers an increasing demand for data collection and data sharing of private and public information at the expense of the user's privacy.…”

Section: Opportunities and Challenges Of Ai In The Health Care Safetymentioning

confidence: 99%

“…This triggers an increasing demand for data collection and data sharing of private and public information at the expense of the user's privacy. 28 Medical ethics, such as an individual's right to privacy, may be threatened by AI and big data features because of the collection and storage of data from various sources. Furthermore, security and safety of vital information may be put at risk by misuse of medicolegal algorithms by hackers for developing autonomous techniques.…”

Section: Opportunities and Challenges Of Ai In The Health Care Safetymentioning

confidence: 99%

Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges

2019

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There is a growing awareness that artificial intelligence (AI) has been used in the analysis of complicated and big data to provide outputs without human input in various health care contexts, such as bioinformatics, genomics, and image analysis. Although this technology can provide opportunities in diagnosis and treatment processes, there still may be challenges and pitfalls related to various safety concerns. To shed light on such opportunities and challenges, this article reviews AI in health care along with its implication for safety. To provide safer technology through AI, this study shows that safe design, safety reserves, safe fail, and procedural safeguards are key strategies, whereas cost, risk, and uncertainty should be identified for all potential technical systems. It is also suggested that clear guidance and protocols should be identified and shared with all stakeholders to develop and adopt safer AI applications in the health care context.

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Medical ethics considerations on artificial intelligence

Cited by 184 publications

References 13 publications

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges

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