Machine learning applications in healthcare and the role of informed consent: Ethical and practical considerations

Lorenzini, Giorgia; Shaw, David; Ossa, Laura Arbelaez; Elger, Bernice Simone

doi:10.1177/14777509221094476

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…AI can have an enormous role in shaping doctors' decisions, so doctors may be required to inform their patients when AI is included in the clinical evaluation. 48 Providing this type of information to patients may help them understand better the reasons for a diagnosis, the different alternatives, and the prognosis. As a consequence, patients would be better positioned to participate in the decision-making process.…”

Section: Doctor-patient Communication and Autonomy With Aimentioning

confidence: 99%

Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making

et al. 2023

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Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI.

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Section: Doctor-patient Communication and Autonomy With Aimentioning

confidence: 99%

Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making

et al. 2023

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“…Unauthorized use of ML, coupled with its fluctuating accuracy, might lead to complications in educational environments. Establishing clear protocols for data security and privacy becomes imperative to safeguard sensitive patient information [52]. Regular updates and continuous training for medical professionals on evolving ML technologies are essential to ensure safe and effective integration into healthcare practices.…”

Section: Limitationmentioning

confidence: 99%

Machine learning's impact on medical education and research: beneficial or detrimental?

Subramaniam,

Gounder,

Mahmood

et al. 2024

IJPHS

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Machine learning (ML), an AI chatbot developed by OpenAI, has the potential to revolutionize medical education by aiding in locating scholarly publications, condensing them, producing automatic drafts, summarising articles, and translating information from various languages. Still ethical concerns need to be governed and closely supervised in scientific literature. ML has become a valuable tool for medical research and teaching due to its ability to generate responses that closely resemble human responses when faced with difficult medical questions. It has disadvantages such as the potential dissemination of inaccurate or prejudiced data and excessive dependence on technology in medical instruction, deteriorating analytical reasoning and clinical judgment abilities. ML can aid in various aspects of medical education, including curriculum building, tutoring, test preparation, medical research, simulation, and continuing medical education. This article explores the transformative impact of ML in the medical field, focusing on medical data analysis, rewards in medical education, enhanced diagnosis, and creative content generation. It delves into ML applications for medical learners and educators, including interactive simulations, cooperation enhancement, and clinical vignettes. The article also addresses ML's role in patient care, along with strategies, challenges, and limitations in its implementation.

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“…The 73 included studies covered a broad spectrum of medical contexts, summarised with the main ethical issues raised and key findings in Supplementary Table S2. Briefly, the most frequently addressed areas were as follows: the general use of AI for healthcare (36 studies) , the use of AI in decision support systems (eight studies) [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], big data (four studies) [95][96][97][98], robotics (seven studies) [99][100][101][102][103][104][105] and adaptive AI (one study) [106]. The remaining studies addressed the following: rehabilitation [107], medical education [108], monitoring technology for the elderly [109], mental health [110], radiation technology [111], chatbots [112], health apps [113] and healthcare in low-and middleincome countries [114].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…This was a key discussion point which was raised in depth in several explorations of transparency. Indeed, a critical and somewhat unique challenge raised by the nature of AI-driven technologies is the extent to which it is possible to understand how the underlying AI system works or reaches its output [49,60,62,64,66,70,85,86,88,92]. Many studies called for this 'explicability' or 'explainability' as a key principle to be met for AI health technologies [60,64], while others emphasised the need for contextual explainability [88].…”

Section: Transparencymentioning

confidence: 99%

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices

Maccaro,

Stokes,

Statham

et al. 2024

JPM

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The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.

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Machine learning applications in healthcare and the role of informed consent: Ethical and practical considerations

Cited by 7 publications

References 30 publications

Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making

Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making

Machine learning's impact on medical education and research: beneficial or detrimental?

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices

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