Reference | Type of study | Aim of study | Country of origin |
---|---|---|---|
(Alrassi 2021) [26] | Scholarly perspective | Presents some of the opportunities and challenges that AI provide. Explains how the role of physicians will evolve in an AI-augmented care environment | USA |
(Amann, Blasimme et al. 2020) [27] | Conceptual and ethical analysis | Provides an assessment of the role of explainability in medical AI and ethically analyses what explainability means for the adoption of AI-driven tools into clinical practice | Switzerland, Germany, UK |
(Aminololama-Shakeri and López 2019) [8] | Opinion piece | Examines what AI means for breast imaging radiologists and the doctor-patient relationship | USA |
(Arnold 2021) [28] | Ethical analysis | Analyses bioethically AI systems and their impact on doctors and patients | Australia |
(Banja 2019) [29] | Opinion piece | Advocates for a new field of ethics engaging specifically with health applications, and engages with commonly made bioethical criticism about AI in healthcare | USA |
(Bjerring and Busch 2021) [30] | Ethical analysis | Investigates ethically the impact of black-box AI tools on the practice of medicine and patient-centred care | Denmark |
(Carter, Rogers et al. 2020) [7] | Research article | Investigates the ethical, legal and social ramifications of using artificial intelligence tools in breast cancer care | Australia |
(Chen 2017) [9] | Opinion piece with a case study (University of Hong Kong) | Investigates the role of doctors in future healthcare and the direction medical schools should take to prepare their graduates, in an Asian context | Hong Kong |
(Dagher, Shi et al. 2020) [31] | Exploratory review and opinion piece | Assesses the role of wearables in cardiology and outlining the benefits associated with their use | USA |
(Davenport and Kalakota 2019) [3] | Research article | Identifies the potential of the use of AI in healthcare and the related potential ethical implications | USA |
(Eysenbach, Wright et al. 2018) [32] | Randomised controlled trial (n = 75) | Randomised controlled trial with 75 participants recruited across the United States in order to assess the feasibility and efficacy of using an integrative psychological AI, Tess, to reduce self-identified symptoms of depression and anxiety in college students | USA |
(Fogel and Kvedar 2018) [18] | Perspective | Proposes a perspective that AI tools will open the way for a more unified, human experience | USA |
(Grote and Berens 2020) [33] | Ethical analysis | Investigates ethically the use of AI tools in clinical decision-making and identification of potential pitfalls of involving machine learning in healthcare | Germany |
(Hagendorff and Wezel 2019) [34] | Overview article | Provides a general overview of the current problems that AI and machine learning research and development must deal with | Germany |
(Ho 2019) [4] | Opinion piece | Explores the ethical challenges posed by AI tools in healthcare and suggests solutions | ? |
(Hung, Chen et al. 2021) [35] | Opinion piece | Outlines the benefits of using AI tools in the field of urology | USA |
(Johnston 2018) [36] | Commentary | Explores the training needs of future physicians in the age of artificial intelligence | USA |
(Jotterand and Bosco 2020) [25] | Commentary | Outlines the conditions necessary for AI to be ethically integrated in healthcare systems | USA |
(Karches 2018) [37] | Philosophical analysis | Philosophically analyses why AI tools should not replace human doctors’ judgements | USA |
(Kerasidou 2020) [38] | Ethical analysis | Analyses ethically how AI has the potential to fundamentally alter the way in which empathy, compassion and trust are currently regarded and practised in health care | United Kingdom |
(Kim, Jones et al. 2019) [39] | Opinion piece | Discusses effects that new technological developments, such as AI, have had on the profession of psychiatry and how teachers can teach trainee psychiatrists the best practices | USA |
(Kolanska, Chabbert-Buffet et al. 2021) [40] | Overview article | Summarises AI use in healthcare, its technical, professional, and ethical shortcomings and assesses of how it ought to be used | France |
(Kool, Laranjo et al. 2019) [41] | Survey (n = 720) | Conducts a web-based survey of 720 UK GPs' perspectives on whether technology will ever completely replace doctors in providing primary care tasks | United Kingdom |
(Lagrew and Jenkins 2015) [42] | Overview and opinion piece | Outlines the future of obstetrics/gynaecology in 2020 including computer-aided diagnoses and proposes a way to thrive in the new system | USA |
(Liu, Keane et al. 2018) [43] | Commentary | Outlines how to prepare the future generation of doctors to practice in a health system enabled by artificial intelligence while providing humanity to the machine-patient relationship | United Kingdom |
(Luxton 2014) [44] | Review (number of papers used: unspecified) | Identifies and reviews ethical concerns associated with AI care providers (AICPs) in mental health care and other professions. Makes recommendations for the development of ethical codes and the design of AICPs | USA |
(Mabillard, Demartines et al. 2021) [45] | Perspective | Discusses the issue of preserving trusting and high-quality relationships between doctors and patients in an era of spread of online information and demands related to accountability placed on healthcare professionals | Belgium, Switzerland |
(Manrique de Lara and Peláez-Ballestas 2020) [46] | Narrative review (number of papers used: unspecified) | Provides a narrative review of the bioethical perspectives of big data with a specific focus on the field of Rheumatology | Mexico |
(McDougall 2019 [24]) | Ethical analysis | Conducts an ethical analysis of the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations | Australia |
(Mihai 2019 [47]) | Ethical analysis | Investigates ethically which, if any, aspects of medicine—currently or in the future—can and ought to be left in the hands of AI | USA |
(Molnár-Gábor 2020) [48] | Research article | Examines the practical and ethical issues that the application of AI raises for people and society | Germany |
(Nelson, Pérez-Chada et al. 2020) [10] | Qualitative study using semi-structured interviews (n = 48) | Investigates how patients view the usage of AI for skin cancer detection and how they conceptualise the technology. Qualitative study with semi structured interviews conducted in hospitals in Boston, USA. 48 patients were enrolled | USA |
(Niel and Bastard 2019) [22] | Perspective | Provides an overview of evidence on medical artificial intelligence relevant to the field of nephrology. Defines core concepts, recent clinical applications and provides a perspective on future considerations including ethical issues arising | France |
(Printz 2017) [49] | Commentary | Summarises the evidence of the time saving capabilities of Watson for Oncology and provides a perspective on AI as an assistant for treatment decisions | USA |
(Rainey and Erden 2020) [50] | Ethical analysis | Identifies issues with the application of neural technologies in psychiatry and urges caution, especially regarding normative issues | UK |
(Sparrow and Hatherley 2020) [23] | Opinion piece | Provides a critical analysis of the positive discourse surrounding the doctor-patient relationship and implementation of AI in healthcare | Australia |
(Szalai 2020) [51] | Research article | Explores theoretically and practically the possibility of AI-based addendum therapy for borderline personality disorder and identifies its potential advantages and limitations | Hungary |
(Trachsel, Gaab et al.) [52] | Research article | Explores the use of chatbots and AI tools as supplements to psychotherapy delivered by humans, and as supervised primary treatments. Discusses how ethical guidelines and standards for AI in mental health are relevant in the ethics of AI in psychotherapy | Switzerland, USA |
(Triberti, Durosini et al. 2020) [53] | Perspective | Explores the “third wheel” effect that AI introduces in healthcare and identifies the impact of this effect created by AI on the healthcare process, with a focus on future medical practice | Italy |
(Tripti and Lalitbhushan 2020) [54] | Opinion piece | Explores the future role of medical doctors in an AI-augmented environment and the related implications on medical education | India |
(Wartman 2019) [55] | Commentary | Identifies challenges facing medical education and ways forward to address these challenges, including the preservation of the doctor-patient relationship in an AI-augmented world | USA |
(Wartman 2019) [56] | Opinion piece | Explores ethically how medical education systems should adapt to the integration of AI systems in healthcare | USA |
(Young, Amara et al. 2021) [57] | Mixed-method systematic review (n = 23) | Explores patient and general public attitudes towards clinical artificial intelligence using a mixed-method systematic review in biomedical and computational databases. 23 papers met the inclusion criteria | USA |
(Yun, Lee et al. 2021) [58] | Behavioural study (n = 350) and neural study (n = 22) | Explores (1) Behavioural and (2) neural consumer responses to human doctors and medical artificial intelligence. Study (1) recruited 350 Amazon Mechanical Turk (MTurk) and study (2) recruited 22 participants in their twenties | Korea |
(Žaliauskaitė 2020) [59] | Theoretical analysis | Discusses challenging aspects of patients’ right to autonomy in the context of technologies and innovation and the role of the implementation of legal instruments against this background | Lithuania |