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On the ethical and moral dimensions of using artificial intelligence for evidence synthesis.
Artificial intelligence (AI) is increasingly being used in the field of medicine and healthcare. However, there are no articles specifically examining ethical and moral dimensions of AI use for evidence synthesis. This article attempts to fills this gap. In doing so, I deploy in written form, what in Bengali philosophy and culture, is the Adda (আড্ডা) approach, a form of oral exchange, which involves deep but conversational style discussion. Adda developed as a form of intellectual resistance against the cultural hegemony of British Imperialism and entails asking provocative question to encourage critical discourse.The raison d'être for using AI is that it would enhance efficiency in the conduct of evidence synthesis, thus leading to greater evidence uptake. I question whether assuming so without any empirical evidence is ethical. I then examine the challenges posed by the lack of moral agency of AI; the issue of bias and discrimination being amplified through AI driven evidence synthesis; ethical and moral dimensions of epistemic (knowledge-related) uncertainty on AI; impact of knowledge systems (training of future scientists, and epistemic conformity), and the need for looking at ethical and moral dimensions beyond technical evaluation of AI models. I then discuss ethical and moral responsibilities of government, multi-laterals, research institutions and funders in regulating and having an oversight role in development, validation, and conduct of evidence synthesis. I argue that industry self-regulation for responsible use of AI is unlikely to address ethical and moral concerns, and that there is a need to develop legal frameworks, ethics codes, and of bringing such work within the ambit of institutional ethics committees to enable appreciation of the complexities around use of AI for evidence synthesis, mitigate against moral hazards, and ensure that evidence synthesis leads to improvement of health of individuals, nations and societies.
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Extending the RIGHT statement for reporting adapted practice guidelines in healthcare: the RIGHT-Ad@pt Checklist protocol
Introduction The adaptation of guidelines is an increasingly used methodology for the efficient development of contextualised recommendations. Nevertheless, there is no specific reporting guidance. The essential Reporting Items of Practice Guidelines in Healthcare (RIGHT) statement could be useful for reporting adapted guidelines, but it does not address all the important aspects of the adaptation process. The objective of our project is to develop an extension of the RIGHT statement for the reporting of adapted guidelines (RIGHT-Ad@pt Checklist). Methods and analysis To develop the RIGHT-Ad@pt Checklist, we will use a multistep process that includes: (1) establishment of a Working Group; (2) generation of an initial checklist based on the RIGHT statement; (3) optimisation of the checklist (an initial assessment of adapted guidelines, semistructured interviews, a Delphi consensus survey, an external review by guideline developers and users and a final assessment of adapted guidelines); and (4) approval of the final checklist. At each step of the process, we will calculate absolute frequencies and proportions, use content analysis to summarise and draw conclusions, discuss the results, draft a report and refine the checklist. Ethics and dissemination We have obtained a waiver of approval from the Clinical Research Ethics Committee at the Hospital de la Santa Creu i Sant Pau (Barcelona, Spain). We will disseminate the RIGHT-Ad@pt Checklist by publishing into a peer-reviewed journal, presenting to relevant stakeholders and translating into different languages. We will continuously seek feedback from stakeholders, surveil new relevant evidence and, if necessary, update the checklist. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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