ethique_IA

Ethical Use of Artificial Intelligence for Scientific Writing: Current Trends.

Wed, 01 May 2024 04:00:00 +0000

Journal of Human Lactation; 05/01/2024
(AN 176581939); ISSN: 08903344
CINAHL Complete

Ethics of using generative pretrained transformer and artificial intelligence systems for patient prior authorizations.

Wed, 01 May 2024 04:00:00 +0000

Journal of the American Academy of Dermatology; 05/01/2024
(AN 176561942); ISSN: 01909622
CINAHL Complete

Artificial Intelligence and Ethical Frameworks in Pediatrics

Wed, 17 Apr 2024 08:29:14 +0000

Author Names: Liu K.

Database Source: Embase Daily Updates

Journal Title: JAMA Pediatrics,JAMA Pediatrics

Article Title: Artificial Intelligence and Ethical Frameworks in Pediatrics

Year: 2024

Issue:

Volume:

Abstract:

Ethical use of artificial intelligence to prevent sudden cardiac death: an interview study of patient perspectives.

Thu, 04 Apr 2024 04:00:00 +0000

BMC Medical Ethics; 04/04/2024
(AN 176609788); ISSN: 14726939
CINAHL Complete

POST-RADIATION PERCUTANEOUS CORONARY INTERVENTION MORTALITY, COST, COMPLICATIONS, AND DISPARITIES: ARTIFICIAL INTELLIGENCE-AUGMENTED COST EFFECTIVENESS AND COMPUTATIONAL ETHICAL ANALYSIS ACROSS 67,350 CARDIO-ONCOLOGY HOSPITALIZATIONS.

Wed, 03 Apr 2024 04:00:00 +0000

Journal of the American College of Cardiology (JACC); 04/03/2024
(AN 176124993); ISSN: 07351097
CINAHL Complete

Consulting ChatGPT: Ethical dilemmas in language model artificial intelligence.

Mon, 01 Apr 2024 04:00:00 +0000

Journal of the American Academy of Dermatology; 04/01/2024
(AN 175913274); ISSN: 01909622
CINAHL Complete

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care.

Thu, 28 Mar 2024 04:00:00 +0000

JAMA Network Open; 03/28/2024
Key Points: Question: What are oncologists' views on ethical issues associated with the implementation of artificial intelligence (AI) in cancer care? Findings: In this cross-sectional survey study, 84.8% of US oncologists reported that AI needs to be explainable by oncologists but not necessarily patients, and 81.4% agreed that patients should consent to AI use for cancer treatment decisions. Less than half (47.1%) of oncologists viewed medico-legal problems from AI use as physicians' responsibility, and although most (76.5%) reported feeling responsible for protecting patients from biased AI, few (27.9%) reported feeling confident in their ability to do so. Meaning: This study suggests that concerns about ethical issues, including explainability, patient consent, and responsibility, may impede optimal adoption of AI into cancer care. Importance: Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. Objective: To evaluate oncologists' views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. Design, Setting, and Participants: This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan & Provider Enumeration System. Main Outcomes and Measures: The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Results: Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. Conclusions and Relevance: In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise. This cross-sectional survey study evaluates oncologists' views on the ethical domains of the use of artificial intelligence (AI) in clinical care, including familiarity, predictions, explainability, bias, deference, and responsibilities.
(AN 176330842); ISSN: 25743805
CINAHL Complete

Academic machine learning researchers' ethical perspectives on algorithm development for health care: a qualitative study.