Tools to foster responsibility in digital solutions that operate with or without artificial intelligence: A scoping review for health and innovation policymakers.

BACKGROUND: Digital health solutions that operate with or without artificial intelligence (D/AI) raise several responsibility challenges. Though many frameworks and tools have been developed, determining what principles should be translated into practice remains under debate. This scoping review aims to provide policymakers with a rigorous body of knowledge by asking: 1) what kinds of practice-oriented tools are available?; 2) on what principles do they predominantly rely?; and 3) what are their limitations? METHODS: We searched six academic and three grey literature databases for practice-oriented tools, defined as frameworks and/or sets of principles with clear operational explanations, published in English or French from 2015 to 2021. Characteristics of the tools were qualitatively coded and variations across the dataset identified through descriptive statistics and a network analysis. FINDINGS: A total of 56 tools met our inclusion criteria: 19 health-specific tools (33.9%) and 37 generic tools (66.1%). They adopt a normative (57.1%), reflective (35.7%), operational (3.6%), or mixed approach (3.6%) to guide developers (14.3%), managers (16.1%), end users (10.7%), policymakers (5.4%) or multiple groups (53.6%). The frequency of 40 principles varies greatly across tools (from 0% for 'environmental sustainability' to 83.8% for 'transparency'). While 50% or more of the generic tools promote up to 19 principles, 50% or more of the health-specific tools promote 10 principles, and 50% or more of all tools disregard 21 principles. In contrast to the scattered network of principles proposed by academia, the business sector emphasizes closely connected principles. Few tools rely on a formal methodology (17.9%). CONCLUSION: Despite a lack of consensus, there is a solid knowledge-basis for policymakers to anchor their role in such a dynamic field. Because several tools lack rigour and ignore key social, economic, and environmental issues, an integrated and methodologically sound approach to responsibility in D/AI solutions is warranted.

Artificial Intelligence and Ethics in Dentistry: A Scoping Review.

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.