Artificial intelligence-based clinical decision support systems have a potential to improve clinical practice, but they may have a negative impact on the physician-patient dialogue, because of the control problem. Physician-patient dialogue depends on human qualities such as compassion, trust, and empathy, which are shared by both parties. These qualities are necessary for the parties to reach a shared understanding -the merging of horizons- about clinical decisions. The patient attends the clinical encounter not only with (...) a malfunctioning body, but also with an ‘unhomelike’ experience of illness that is related to a world of values and meanings, a life-world. Making wise individual decisions in accordance with the patient’s life-world requires not only scientific analysis of causal relationships, but also listening with empathy to the patient’s concerns. For a decision to be made, clinical information should be interpreted considering the patient’s life-world. This side of clinical practice is not a job for computers, and they cannot be final decision-makers. On the other hand, in the control problem users blindly accept system output because of over-reliance, rather than evaluating it with their own judgement. This means over-reliant parties leave their place in the dialogue to the system. In this case, the dialogue may be disrupted and mutual trust may be lost. Therefore, it is necessary to design decision support systems to avoid the control problem and to limit their use when this is not possible, in order to protect the physician-patient dialogue. (shrink)

Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals’ attitudes to potential (...) changes of responsibility and decision-making authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees’ reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians’ responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS. (shrink)

Research regarding the drivers of acceptance of clinical decision support systems by physicians is still rather limited. The literature that does exist, however, tends to focus on problems regarding the user-friendliness of CDSS. We have performed a thematic analysis of 24 interviews with physicians concerning specific clinical case vignettes, in order to explore their underlying opinions and attitudes regarding the introduction of CDSS in clinical practice, to allow a more in-depth analysis of factors underlying acceptance (...) of CDSS. We identified three general themes from the results. First, ‘the perceived role of the AI’, including items referring to the tasks that may properly be assigned to the CDSS according to the respondents. Second, ‘the perceived role of the physician’, referring to the aspects of clinical practice that were seen as being fundamentally ‘human’ or non-automatable. Third, ‘concerns regarding AI’, including items referring to more general issues that were raised by the respondents regarding the introduction of CDSS in general and/or in clinical medicine in particular. Apart from the overall concerns expressed by the respondents regarding user-friendliness, we will explain how our results indicate that our respondents were primarily occupied by distinguishing between parts of their job that should be automated and aspects that should be kept in human hands. We refer to this distinction as ‘the division of clinical labor.’ This division is not based on knowledge regarding AI or medicine, but rather on which parts of a physician’s job were seen by the respondents as being central to who they are as physicians and as human beings. Often the respondents’ view that certain core parts of their job ought to be shielded from automation was closely linked to claims concerning the uniqueness of medicine as a domain. Finally, although almost all respondents claimed that they highly value their final responsibility, a closer investigation of this concept suggests that their view of ‘final responsibility’ was not that demanding after all. (shrink)

Integrating artificial intelligence (AI) into healthcare can potentially revolutionize how clinical decisions are made. Advancements in AI-driven Clinical Decision Support Systems (AI_CDSS) are enh...

Background Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision-making, they also raise significant ethical concerns. This study aims to explore healthcare professionals’ perspectives on the ethical implications of using AI-CDSS for healthcare resource allocation. Methods We conducted semi-structured qualitative interviews with 23 healthcare professionals, including physicians, nurses, administrators, and medical ethicists in Turkey. Interviews focused on participants’ views (...) regarding the use of AI-CDSS in resource allocation, potential ethical challenges, and recommendations for responsible implementation. Data were analyzed using thematic analysis. Results Participant responses are clustered around five pre-determined thematic areas: (1) balancing efficiency and equity in resource allocation, (2) the importance of transparency and explicability in AI-CDSS, (3) shifting roles and responsibilities in clinical decision-making, (4) ethical considerations in data usage and algorithm development, and (5) balancing cost-effectiveness and patient-centered care. Participants acknowledged the potential of AI-CDSS to optimize resource allocation but expressed concerns about exacerbating healthcare disparities, the need for interpretable AI models, changing professional roles, data privacy, and maintaining individualized care. Conclusions The integration of AI-CDSS into healthcare resource allocation presents both opportunities and significant ethical challenges. Our findings underscore the need for robust ethical frameworks, enhanced AI literacy among healthcare professionals, interdisciplinary collaboration, and rigorous monitoring and evaluation processes. Addressing these challenges proactively is crucial for harnessing the potential of AI-CDSS while preserving the fundamental values of equity, transparency, and patient-centered care in healthcare delivery. (shrink)

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a (...) class='Hi'>clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created and illustrates the overall process on a small set of case studies. (shrink)

Background Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions (...) that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice. (shrink)

Making good decisions in extremely complex and difficult processes and situations has always been both a key task as well as a challenge in the clinic and has led to a large amount of clinical, legal and ethical routines, protocols and reflections in order to guarantee fair, participatory and up-to-date pathways for clinical decision-making. Nevertheless, the complexity of processes and physical phenomena, time as well as economic constraints and not least further endeavours as well as achievements in (...) medicine and healthcare continuously raise the need to evaluate and to improve clinical decision-making. This article scrutinises if and how clinical decision-making processes are challenged by the rise of so-called artificial intelligence-driven decision support systems (AI-DSS). In a first step, this article analyses how the rise of AI-DSS will affect and transform the modes of interaction between different agents in the clinic. In a second step, we point out how these changing modes of interaction also imply shifts in the conditions of trustworthiness, epistemic challenges regarding transparency, the underlying normative concepts of agency and its embedding into concrete contexts of deployment and, finally, the consequences for (possible) ascriptions of responsibility. Third, we draw first conclusions for further steps regarding a ‘meaningful human control’ of clinical AI-DSS. (shrink)

BackgroundMachine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion (...) by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician–patient relationship might be affected by the employment of ML_CDSS.Main textProfessionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.ConclusionsUsing ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making. (shrink)

Statistics is the science of collection, summarizing, presentation and interpretation of data. Moreover, it yields methods used in the verification of research hypotheses. The presence of a statistician in a research group remarkably improves both the quality of design and research and the optimization of financial resources. Moreover, the involvement of a statistician in a research team helps the physician to effectively utilize the time and energy spent on diagnosing, which is an important aspect in view of limited healthcare resources. (...) Precise, properly designed and implemented Computerized Clinical Decision Support Systems certainly lead to the improvement of healthcare and the quality of medical services, which increases patient satisfaction and reduces financial burdens on healthcare systems. (shrink)

Machine learning-driven decision support systems (ML_CDSS) are poised for increasing presence and influence in clinical contexts. Salloch and Eriksen (2024) make two key arguments, that together bu...

Physicians and other healthcare professionals are increasingly finding ways to use artificial intelligent decision support systems in their work. IBM Watson Health, for example, is a commercially available technology that is providing AI-DDS services in genomics, oncology, healthcare management and more.1 AI’s ability to scan massive amounts of data, detect patterns, and derive solutions from data is vastly more superior than that of humans. AI technology is undeniably integral to the future of healthcare and public health, and thoughtful (...) consideration of the legal, ethical and moral issues surrounding this technology is a must. The authors of the article, Responsibility, Second Opinions, and Peer-Disagreement—Ethical and Epistemological Challenges of Using AI in Clinical Diagnostic Contexts, provide an informed discussion of how AI-DSS may be used, both practically and ethically, to assist healthcare professionals in cooperative diagnostic processes2. The authors propose a process, whereby AI-DSS would provide a physician a second opinion, and when there is a mismatch between opinions, another physician would provide a third opinion. This approach maintains a ‘physician-in-charge’ perspective, and suggests that decision-making must ultimately be made by a person. This approach is also consistent with the ‘physician in-the-loop’ concept, such that even when increasingly autonomous AI-DSSs are put to use, physicians will still be providing checks and overseeing clinical decisions. The authors conceptualise AI as a replacement of human cognitive labour. That is, AI is used to supplant a human professional in particular functions …. (shrink)

Recent developments in ML driven decision support systems have played an important role in clinical decision making, whether one consider clinical decisions that involves image recognition (Berge e...

In this article I consider two pertinent questions that practitioners must consider when they deploy an algorithmic system as support in clinical shared decision‐making. The first question concerns how to interpret and assess the significance of different performance measures for clinical decision‐making. The second question concerns the professional obligations that practitioners have to communicate information about the quality of an algorithm's output to patients in light of the principles of autonomy, beneficence, and justice. In (...) the article I review the four standard performance measures used to evaluate and validate algorithms, outline their role in the discussion of algorithmic fairness, and discuss the professional responsibilities that practitioners face when communicating information about these measures to patients. (shrink)

Resnik and Pugh recently explored the ethical implications of routinely integrating environmental concerns into clinical decision-making. While we share their concern for the holistic well-being of patients, our response offers a different clinical and bioethical stance on green informed consent and patient autonomy. Contrary to the authors’ lack of data to support their concerns about provider and patient willingness to engage in climate-related conversations, we provide evidence supporting their sustainability engagement and stress the importance of a (...) proactive, anticipatory approach in healthcare to align with evolving societal values. If climate change is perceived as a politicised issue, though it is not inherently so, healthcare providers are professionally trained to address sensitive subjects and have a duty to inform patients about potential health risks. Recognising the environmental crisis as a health crisis underscores the direct connection between environmental hazards and patients’ well-being. Our perspective advocates for integrating individual considerations, societal responsibilities and systemic changes to promote environmentally sustainable healthcare. (shrink)

BackgroundHealthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.MethodsPubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was (...) title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.ResultsArtificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.ConclusionsThe prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far.Trial registrationThis review is registered at Open Science Framework (https://osf.io/wvcs9). (shrink)

Clinical ethical support services (CESS) represent a multifaceted field of aims, consultancy models, and methodologies. Nevertheless, the overall aim of CESS can be summed up as contributing to healthcare of high ethical standards by improving ethically competent decision-making in clinical healthcare. In order to support clinical care adequately, CESS must pay systematic attention to all real-life ethical issues, including those which do not fall within the ‘favourite’ ethical issues of the day. In this paper (...) we attempt to capture a comprehensive overview of categories of ethical tensions in clinical care. We present an analytical exposition of ethical structural features in judgement-based clinical care predicated on the assumption of the moral equality of human beings and the assessment of where healthcare contexts pose a challenge to achieving moral equality. The account and the emerging overview is worked out so that it can be easily contextualized with regards to national healthcare systems and specific branches of healthcare, as well as local healthcare institutions. By considering how the account and the overview can be applied to i) improve the ethical competence of healthcare personnel and consultants by broadening their sensitivity to ethical tensions, ii) identify neglected areas for ethical research, and iii) clarify the ethical responsibility of healthcare institutions' leadership, as well as specifying required institutionalized administration, we conclude that the proposed account should be considered useful for CESS. (shrink)

Many clinical ethics support services do not involve patients. This is surprising because of the broad commitment to provide patient-centered healthcare. Clinical ethics support services are a component of the healthcare system and have an influence on patient care, and should therefore align with the regulatory and ethical requirements of patient-centered care, just process and cultural competence. First, in order to achieve good patient care, it is essential to involve patients in making their own healthcare (...) decisions. Second, just ethical deliberation requires that the deliberating parties have access to complete, accurate and unbiased information – this is best achieved through direct patient input. These arguments are especially compelling in the case of non-dominant cultural and ethnic groups. Here, we argue that when a clinician refers a case to a clinical ethics support services for consideration, and that case involves a conflict between the views or values of the clinician and the patient, the patient is entitled to, at a minimum: (a) be informed of the referral; (b) have the opportunity to speak directly to the clinical ethics support services prior to deliberation; (c) be provided with any opinion directly from the clinical ethics support services; and (d) have the opportunity to speak to a clinical ethics support services member after the deliberation. We use New Zealand as a case study to support and extrapolate the ethical principles and the minimum patient involvement requirements. We draw on our empirical research with senior doctors at a tertiary New Zealand hospital. Clinicians in our study indicated that they would feel bound by clinical ethics support service advice and that the clinical ethics support service has a significant impact on patient care in the case it reviews. (shrink)

Although clinical ethics support services are becoming increasingly prevalent in Europe and North America, they remain an uncommon feature of the Irish healthcare system and Irish health professionals lack formal support when faced with ethically challenging cases. We have developed a variant on existing clinical ethics decision-making tools which is designed to build capacity and confidence amongst Irish practitioners and enable them to confront challenging situations in the absence of any dedicated support structure. (...) The tool provided below follows a transparent stepwise procedure which avoids an overcomplicated analysis while remaining sensitive to the complexity of the issues addressed. The novelty of the tool lies in the explicit association it makes between each step in the process and one or more responsibilities based on established norms of ethical decision-making in the public domain. (shrink)

Clinical ethical support services (CESS) represent a multifaceted field of aims, consultancy models, and methodologies. Nevertheless, the overall aim of CESS can be summed up as contributing to healthcare of high ethical standards by improving ethically competent decision‐making in clinical healthcare. In order to support clinical care adequately, CESS must pay systematic attention to all real‐life ethical issues, including those which do not fall within the ‘favourite’ ethical issues of the day. In this paper (...) we attempt to capture a comprehensive overview of categories of ethical tensions in clinical care. We present an analytical exposition of ethical structural features in judgement‐based clinical care predicated on the assumption of the moral equality of human beings and the assessment of where healthcare contexts pose a challenge to achieving moral equality. The account and the emerging overview is worked out so that it can be easily contextualized with regards to national healthcare systems and specific branches of healthcare, as well as local healthcare institutions. By considering how the account and the overview can be applied to i) improve the ethical competence of healthcare personnel and consultants by broadening their sensitivity to ethical tensions, ii) identify neglected areas for ethical research, and iii) clarify the ethical responsibility of healthcare institutions' leadership, as well as specifying required institutionalized administration, we conclude that the proposed account should be considered useful for CESS. (shrink)

Background Although the importance of clinical ethics in contemporary clinical environments is established, development of formal clinical ethics services in the Australia health system has, to date, been ad hoc. This study was designed to systematically follow and reflect upon the first 18 months of activity by a newly established service, to examine key barriers and facilitators to establishing a new service in an Australian hospital setting. Methods: how the study was performed and statistical tests used (...) A qualitative case study approach was utilised. The study gathered and analysed data using observations of service committee meetings, document analysis of agendas and minutes, and semi-structured interviews with committee members to generate semantic themes. By interpreting the thematic findings in reference to national capacity building resources, this study also aimed to provide practice-based reflections for other health agencies. Results: the main findings An overarching theme identified in the data was a strong commitment to supporting clinicians facing difficult patient care decisions and navigating difficult discussions with patients and families. Another key theme was the role of the new clinical ethics support service in providing clinicians with a pathway to raise system-wide issues with the organisation Executive. While there was strong clinical engagement, consumer and community participation remained a challenge, as did unresolved governance issues and a need for clearer policy relationship between the service and the organisation. Conclusions: brief summary and potential implications Considering these themes in relation to the national capacity building resources, the study identifies three areas likely to require ongoing development and negotiation. These are: the role of the clinical ethics support service as a link between the workforce and the Executive; the incorporation of consumers and patients; and ethical reasoning. To improve the effectiveness of the service, it is necessary to increase clarity on the service’s role at the governance and policy level, as well as develop strategies for engaging consumers, patients and families. Finally, the capacity of the service to reflect on complex cases may be enhanced through explicit discussions of various different ethical frameworks and ways of deliberating. (shrink)

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. (shrink)

Within the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as “human in the loop” or “meaningful human control” are often cited as being necessary for ethical legitimacy. In addition, ethical principles usually serve as the major point of reference in ethical guidance documents, stating that conflicts between principles need to be weighed and balanced against each other. Starting from a neo-Kantian viewpoint inspired by Onora O'Neill, this article makes a concrete suggestion of how to (...) interpret the role of the “human in the loop” and to overcome the perspective of rivaling ethical principles in the evaluation of AI in health care. We argue that patients should be perceived as “fellow workers” and epistemic partners in the interpretation of ML_CDSS outputs. We further highlight that a meaningful process of integrating (rather than weighing and balancing) ethical principles is most appropriate in the evaluation of medical AI. (shrink)

Can machine intelligence do clinical ethics? And if so, would applying it to actual medical cases be desirable? In a recent target article (Meier et al. 2022), we described the piloting of our advisory algorithm METHAD. Here, we reply to commentaries published in response to our project. The commentaries fall into two broad categories: concrete criticism that concerns the development of METHAD; and the more general question as to whether one should employ decision-support systems of this kind—the (...) debate we set out to ignite with our target article. (shrink)

Given the nature of the discipline, it might be assumed that clinical psychology is an ethical profession, within which effective ethical decision-making is integral. How then, does this ethical decision-making occur? This paper describes a systematic review of empirical research addressing this question. The paucity of evidence related to this question meant that the scope was broadened to include other professions who deliver talking therapies. This review could support reflective practice about what may be taken into (...) account when making ethical decisions and highlight areas for future research. Using academic search databases, original research articles were identified from peer-reviewed journals. Articles using qualitative ( n = 3), quantitative ( n = 8) and mixed methods ( n = 2) were included. Two theoretical models of aspects of ethical decision-making were identified. Areas of agreement and debate are described in relation to factors linked to the professional, which impacted ethical decision-making. Factors relating to ethical dilemmas, which impacted ethical decision-making, are discussed. Articles were appraised by two independent raters, using quality assessment criteria, which suggested areas of methodological strengths and weaknesses. Comparison and synthesis of results revealed that the research did not generally pertain to current clinical practice of talking therapies or the particular socio-political context of the UK healthcare system. There was limited research into ethical decision-making amongst specific professions, including clinical psychology. Generalisability was limited due to methodological issues, indicating avenues for future research. (shrink)

Clinical judgment, also called clinical reasoning, clinical decision-making, and diagnostic-therapeutic decision-making, lies at the heart of clinical practice and thus medicine. In thepast, clinical judgment was considered the expert task of the physician. But the advent of computers in the 1940s and their use in medicine as of the late 1950s gradually changed this situation. In the 1960s, a new discipline emerged that has come to be termed medical computer science or medical informatics, (...) including clinical informatics. Clinical informatics is concerned with all aspects of the application of computing machinery in clinical research and practice. As one of its accomplishments in clinical practice, the physician’s capacity for clinical judgment is in the process of being turned over to computers. We are told that computers will be responsible for making diagnoses and treatment decisions in a not-too-distant future. This prospect, its historical, methodological and philosophical background, and its impact on health care will be discussed in the present Part VI which divides into these three chapters: 19 Medical Decision-Making 20 Clinical Decision Support Systems 21 Artificial Intelligence in Medicine? (shrink)

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to (...) support decision-making around cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

Recruitment processes for clinical trials are governed by guidelines and regulatory systems intended to ensure participation is informed and voluntary. Although the guidelines and systems provide some protection to potential participants, current recruitment processes often result in limited understanding and experiences of inadequate decision support. Many trials also have high drop-out rates among participants, which are ethically troubling because they can be indicative of poor experiences and they limit the usefulness of the knowledge the trials were designed (...) to generate. Drawing on recent social-psychological and philosophical-ethical research on trial recruitment and patient participation in treatment decision-making, this paper identifies possibilities for improving communicative support for both initial decisions and ongoing participation in clinical trials. It highlights the potential of a shift in thinking about ‘voluntariness’, underpinned by relational understandings of autonomy, to encourage more nuanced judgements about the ethics of communication between trial staff and (potential) participants. The paper suggests that the idea of responsively enabling people to consider invitations or requests to participate in particular trials could serve as a general guide to communication. This might help ensure decisions about trial participation are meaningfully informed and voluntary, and that relationships between trial staff and participants contribute to positive experiences of trial participation and ultimately to the generation of the robust knowledge. (shrink)

The rapid progress and expanding development of machine learning-driven clinical decision support systems (ML_CDSS) have led to calls for involving “humans in the loop” in the design, development,...

Objective Gather information from physicians about factors contributing to unnecessary noninvasive imaging and impact of possible solutions. Methods Qualitative study of 14 physicians using a phenomenological approach and the Theoretical Domains Framework. Results Most participants ( n = 9) self-reported that >10% of the imaging tests they order are unnecessary. External sources of pressure included: peer-review, patient demands, nursing expectations, specialist requests (social demands), as well as prior experience with patient advocates, and the compensation and pension system (environmental context). (...) Internal sources of pressure included reliance on anecdote (emotion), self-doubt about diagnoses (beliefs about capabilities), and fear of missing a diagnosis and of professional liability (beliefs about consequences). Participants expressed both optimism and concern about potential solutions, such as adopting decision support software. Conclusion Physicians are under pressure from multiple sources to order unnecessary imaging. Peer review, nursing expectations, and perceptions about Veteran compensation and pension are newly reported contributing factors. (shrink)

In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second (...) opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a ‘rule of disagreement’ that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments. (shrink)

According to Sutton et al. a “clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information...

The aim of this literature review was to compose a narrative review supported by a systematic approach to critically identify and examine concerns about accountability and the allocation of responsibility and legal liability as applied to the clinician and the technologist as applied the use of opaque AI-powered systems in clinical decision making. This review questions if it is permissible for a clinician to use an opaque AI system in clinical decision making and if a (...) patient was harmed as a result of using a clinician using an AIS’s suggestion, how would responsibility and legal liability be allocated? Literature was systematically searched, retrieved, and reviewed from nine databases, which also included items from three clinical professional regulators, as well as relevant grey literature from governmental and non-governmental organisations. This literature was subjected to inclusion/exclusion criteria; those items found relevant to this review underwent data extraction. This review found that there are multiple concerns about opacity, accountability, responsibility and liability when considering the stakeholders of technologists and clinicians in the creation and use of AIS in clinical decision making. Accountability is challenged when the AIS used is opaque, and allocation of responsibility is somewhat unclear. Legal analysis would help stakeholders to understand their obligations and prepare should an undesirable scenario of patient harm eventuate when AIS were used. (shrink)

The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by these algorithms, and how their real world meaning is translated into machine language and vice versa, how their output is understood by the end user. This “semantic” black box adds to the “mathematical” black box present in many AI systems in which the underlying (...) “reasoning” process is often opaque. In this way, whereas it is often claimed that the use of AI in medical applications will deliver “objective” information, the true relevance or meaning to the end‐user is frequently obscured. This is highly problematic as AI devices are used not only for diagnostic and decision support by healthcare professionals, but also can be used to deliver information to patients, for example to create visual aids for use in shared decision‐making. This paper provides an examination of the range and extent of this problem and its implications, on the basis of cases from the field of intensive care nephrology. We explore how the problematic terminology used in human communication about the detection, diagnosis, treatment, and prognosis of concepts of intensive care nephrology becomes a much more complicated affair when deployed in the form of algorithmic automation, with implications extending throughout clinical care, affecting norms and practices long considered fundamental to good clinical care. (shrink)

Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are (...) often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems. (shrink)

This paper aims to identify the decision support systems and their role on the strategic management development in the Universities- Case Study: Islamic University of Gaza. The descriptive approach was used where a questionnaire was developed and distributed to a stratified random sample. (230) questionnaires were distributed and (204) were returned with response rate (88.7%). The most important findings of the study: The presence of a statistically significant positive correlation between the decision support systems and strategic (...) management in the Islamic University in Gaza, and this shows that there is a role for decision support systems in strategic management development. The senior management is convinced of the importance of strategic management of the organization, and this conviction paid to clearly understand the process of the development of strategic management and thus led to a commitment to this process to improve its performance. There is an impact on the strategic management of the capabilities available for the use of decision support systems, and the type of decision support systems used, while there is little effect of top management support for the use of decision support systems. Also, the results showed that there were no statistically significant differences between the averages of the respondents answers attributed to personal data. The research also concluded a series of recommendations: The Islamic University of Gaza need to develop its infrastructure of decision support systems when it develops the strategic management. It should increase its interest in organizational potential through the necessity of a separate unit for decision support systems. As well as increased interest in the physical and technical capabilities available to decision support systems through continuity in keeping pace with technological means and techniques, continued support of senior management in the organization of strategic management for its positive role on decision support systems, that senior management will continue in the organization in creating a culture of strategic management, and it should encourage employees to participate in the strategic management process. (shrink)

Background Non-invasive prenatal testing (NIPT), which can screen for aneuploidies such as trisomy 21, is being implemented in several public healthcare systems across Europe. Comprehensive communication and information have been highlighted in the literature as important elements in supporting women’s reproductive decision-making and addressing relevant ethical concerns such as routinisation. Countries such as England and France are adopting broadly similar implementation models, offering NIPT for pregnancies with high aneuploidy probability. However, we do not have a deeper understanding of how (...) professionals’ counselling values and practices may differ between these contexts. Methods In this paper, we explore how professionals in England and France support patient decision-making in the provision of NIPT and critically compare professional practices and values. We draw on data from semi-structured interviews with healthcare professionals. Results Both English and French professionals emphasised values relating to patient choice and consent. However, understandings and application of these values into the practice of NIPT provision differed. English interviewees placed a stronger emphasis on interpreting and describing the process of counselling patients and clinical care through a “principle” lens. Their focus was on non-directiveness, standardisation, and the healthcare professional as “decision-facilitator” for patients. French interviewees described their approach through a “procedural” lens. Their focus was on formal consent, information, and the healthcare professional as “information-giver”. Both English and French professionals indicated that insufficient resources were a key barrier in effectively translating their values into practice. Conclusion Our findings illustrate that supporting patient choice in the provision of NIPT may be held as an important value in common on a surface level, but can be understood and translated into practice in different ways. Our findings can guide further research and beneficially inform practice and policy around NIPT provision. (shrink)

Background Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are being increasingly introduced into various domains of health care for diagnostic, prognostic, therapeutic and other purposes. A significant part of the discourse on ethically appropriate conditions relate to the levels of understanding and explicability needed for ensuring responsible clinical decision-making when using AI-CDSS. Empirical evidence on stakeholders’ viewpoints on these issues is scarce so far. The present study complements the empirical-ethical body of research by, on the (...) one hand, investigating the requirements for understanding and explicability in depth with regard to the rationale behind them. On the other hand, it surveys medical students at the end of their studies as stakeholders, of whom little data is available so far, but for whom AI-CDSS will be an important part of their medical practice. Methods Fifteen semi-structured qualitative interviews (each lasting an average of 56 min) were conducted with German medical students to investigate their perspectives and attitudes on the use of AI-CDSS. The problem-centred interviews draw on two hypothetical case vignettes of AI-CDSS employed in nephrology and surgery. Interviewees’ perceptions and convictions of their own clinical role and responsibilities in dealing with AI-CDSS were elicited as well as viewpoints on explicability as well as the necessary level of understanding and competencies needed on the clinicians’ side. The qualitative data were analysed according to key principles of qualitative content analysis (Kuckartz). Results In response to the central question about the necessary understanding of AI-CDSS tools and the emergence of their outputs as well as the reasons for the requirements placed on them, two types of argumentation could be differentiated inductively from the interviewees’ statements: the first type, the clinician as a systemic trustee (or “the one relying”), highlights that there needs to be empirical evidence and adequate approval processes that guarantee minimised harm and a clinical benefit from the employment of an AI-CDSS. Based on proof of these requirements, the use of an AI-CDSS would be appropriate, as according to “the one relying”, clinicians should choose those measures that statistically cause the least harm. The second type, the clinician as an individual expert (or “the one controlling”), sets higher prerequisites that go beyond ensuring empirical evidence and adequate approval processes. These higher prerequisites relate to the clinician’s necessary level of competence and understanding of how a specific AI-CDSS works and how to use it properly in order to evaluate its outputs and to mitigate potential risks for the individual patient. Both types are unified in their high esteem of evidence-based clinical practice and the need to communicate with the patient on the use of medical AI. However, the interviewees’ different conceptions of the clinician’s role and responsibilities cause them to have different requirements regarding the clinician’s understanding and explicability of an AI-CDSS beyond the proof of benefit. Conclusions The study results highlight two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence. These findings should inform the debate on appropriate training programmes and professional standards (e.g. clinical practice guidelines) that enable the safe and effective clinical employment of AI-CDSS in various clinical fields. While current approaches search for appropriate minimum requirements of the necessary understanding and competence, the differences between (future) clinicians in terms of their information and understanding needs described here can lead to more differentiated approaches to solutions. (shrink)

Methodological strategies for the identification and synthesis of ‘evidence’ to support decision‐making in relation to complex healthcare systems and practices This paper addresses the limitations of current methods supporting ‘evidence‐based health‐care’ in relation to complex aspects of care, including those questions that are best supported by descriptive or non‐empirical evidence. The paper identifies some new methods, which may be useful in aiding the synthesis of data in these areas. The methods detailed are broadly divided into those that facilitate (...) the identification of evidence and those that enable the interpretation of the data retrieved. To illustrate some of the issues involved, reference is made to a multimethod review recently completed by the authors, which aimed to identify factors that promote continuity in the transition from child to adult health and social care. It is argued that as healthcare organisations are becoming increasingly preoccupied with the evidence base of practice, such methods may help ensure that aspects of care and approaches that are outside the dominant pharmaco‐medical domain maintain a prominent position on the healthcare agenda while remaining open to external scrutiny. Healthcare professionals who use such approaches need to know their relative utility and benefits to inform clinical decisions, so as to ensure that best practice is observed. (shrink)

Artificial intelligence is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative (...) balancing approach of the Ethics Framework for Big Data in Health and Research. The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper. (shrink)

Based on applied linguistics, this study looked at the decision support system for emphasizing self-assurance in academic writing. From a generic perspective, academic writing has been considered both a process and a product. It has highlighted the planning composite processes, editing, composing, revising, and assessment, which depend upon the familiarity of someone with confidence in their capability for engagement in these activities. As a product, it has focused on the writing results through the product’s characteristics. These contain (...) specific content areas in acceptable depth and well-structured technical vocabulary. Higher education aims to support students in optimizing their potential for achieving satisfactory outcomes. For example, the assessment of grades involves academic writing, contributing to the degree course classification. Students have differences in many respects, such as expectations, background knowledge, and study and learning approaches. There were varying students’ beliefs about what academic writing is for evaluation. Modern-day motivations and theories highlight the significance of students’ confidence in their studies. The role of high confidence can support students to apply more effort toward setting challenging goals. Students may find it more difficult to succeed in higher education if they lack confidence in their academic writing abilities. A DSS has many applications in diverse areas and can play a significant role in the ranking and prioritization process. The current study has considered the DSS for prioritizing self-assurance in academic writing based on applied linguistics. Various criteria were considered for the evaluation of the research. The Super Decision software was used for the experimental process of the proposed research. The results of the study show the effectiveness of the proposed research. (shrink)