Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Magnetoencephalography is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm—the Champagne algorithm with noise learning—which has shown tremendous success in (...) source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications. (shrink)

Machine learning algorithms are expected to improve referral decisions. In this article I discuss the legitimacy of deferring referral decisions in primary care to recommendations from such algorithms. The standard justification for introducing algorithmic decision procedures to make referral decisions is that they are more accurate than the available practitioners. The improvement in accuracy will ensure more efficient use of scarce health resources and improve patient care. In this article I introduce a proceduralist framework for discussing the legitimacy (...) of algorithmic referral decisions and I argue that in the context of referral decisions the legitimacy of an algorithmic decision procedure can be fully accounted for in terms of the instrumental values of accuracy and fairness. I end by considering how my discussion of procedural algorithmic legitimacy relates to the debate on algorithmic fairness. (shrink)

State prescription drug monitoring programs (PDMPs) use proprietary, predictive software platforms that deploy algorithms to determine whether a patient is at risk for drug misuse, drug diversion, doctor shopping, or substance use disorder (SUD). Clinical overreliance on PDMP algorithm‐generated information and risk scores motivates clinicians to refuse to treat—or to inappropriately treat—vulnerable people based on actual, perceived, or past SUDs, chronic pain conditions, or other disabilities. This essay provides a framework for challenging PDMP algorithmic discrimination as disability discrimination (...) under federal antidiscrimination laws, including a new proposed rule interpreting section 1557 of the Affordable Care Act. (shrink)

Health, such as the industry, university or city, is immersed in a process of digital transformation generated by the possibility and technological convergence of the Internet of Things (IoT), Big Data and Artificial Intelligence on the one hand and their consequences on the other: hyperconnectivity, datafication and algorithmization. This is a process of transformation towards what has come to be called Smart Health, Health 4.0 or mHealth. However, despite the enormous potential that underlies the digitization of the healthcare sector, this (...) process can have very negative effects both for the sector and its organizations and for the system’s user. One of the most worrying questions at this moment is the aspect and the force that bioethification is taking; that is, the process by which mass data on the opinions, preferences and behaviors of the hyperconnected users of the cyber-physical assistance system are stored, crossed and processed, on the basis of an utilitarian criterion, to determine through mathematical models what is morally valid. The aim of the present study is to analyze the present and future consequences of bioethification in the health field, as well as to propose guidelines to undertake a socially responsible and morally valid digitalization. Key words: bioethification, moral deliberation, discursive ethics, clinical practice, smart health, smart hospital. (shrink)

While principle-based ethics is well known and widely accepted in psychiatry, much less is known about how decisions are made in clinical practice, which case scenarios exist, and which challenges exist for decision-making. Protocols of the central ethics committee responsible for four psychiatric hospitals over 7 years (N=17) were analysed. While four cases concerned suicide risk in the case of intended hospital discharge, the vast majority (N=13) concerned questions of whether the responsible physician should or should not initiate the (...) use of coercion in patients lacking mental capacity. The committee’s recommendations were non-uniform. Forced feeding and electroconvulsive therapy were endorsed in each one case. In two cases of intermittent loss of capacity due to heavy drinking or intermittent severe suicidal ideation, a self-binding contract was recommended and the use of coercion was considered as justified for a very limited period. In all other cases, most of which involved involuntary treatment, the use of coercion was not endorsed. Without exception, the recommendations were accepted with relief by the physicians and their treatment teams, who feared liability in the event of harm to the patient. Eventually, a model of a decision algorithm was derived from the ethical arguments in the protocols. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate (...) systems medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)

The amount of data available to healthcare practitioners is growing, and the rapid increase in available patient data is becoming a problem for healthcare practitioners, as they are often unable to fully survey and process the data relevant for the treatment or care of a patient. Consequently, there are currently several efforts to develop systems that can aid healthcare practitioners with reading and processing patient data and, in this way, provide them with a better foundation for decision-making about the treatment (...) and care of patients. There are also efforts to develop algorithms that provide suggestions for such decisions. However, the development of these systems and algorithms raises several concerns related to the privacy of patients, the patient–practitioner relationship, and the autonomy of healthcare practitioners. The aim of this article is to provide a foundation for understanding the ethical challenges related to the development of a specific form of data-processing systems, namely clinical algorithms. (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)

In their paper “Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept,” Meier et al. present the design and preliminary results of a proof-of-concept clinical ethics algor...

The robustness of a health system can often be assessed by its response to unpredictable circumstances that demand resourcefulness and resilience. The ongoing COVID-19 pandemic has similarly challe...

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...) In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, (...) arguably, to the lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

Qualitative evidence concerning the relationship between QoL and a wide range of disabilities suggests that subjective judgments regarding other people’s QoL are wrong more often than not and that such judgments by medical practitioners in particular can be biased. Guided by their desire to do good and avoid harm, surgeons often rely on "the eyeball test" to decide whether a patient will or will not benefit from surgery. But the eyeball test can easily harbor a range of implicit judgments and (...) biases against patients with disabilities, including erroneous assumptions about QoL. We critique the use of the "eyeball" test and offer suggestions to reduce bias and ensure that patients’ values are more consistently prioritized in surgical decision-making. (shrink)

The question of the extent to which one can rationally reconstruct the process of medical diagnosis and reduce it to an algorithm is explored. The act of diagnostic insight is such that a computational program cannot ‘catch on’ in the way that a competent diagnostician can. Clinical diagnostic reasoning in a particular case requires as a necessary condition an extraordinarily complex and rich structure of background knowledge as well as an intuitive element, such as is manifest when one ‘catches (...) on’ to a joke. Computers cannot ‘catch on’ due to their limitations in the computational mode. As a consequence, there can be no computer simulation of an essential element in diagnostic procedures. Keywords: clinical judgment, computer diagnosis, intuition, medical decision-making CiteULike Connotea Del.icio.us What's this? (shrink)

Machine learning algorithms have begun to enter clinical settings traditionally resistant to digitalisation, such as psychiatry. This raises questions around how algorithms will be incorporated in professionals’ practices, and with what implications for care provision. This paper addresses such questions by examining the pilot of an algorithm for the prediction of inpatient violence in two acute psychiatric clinics in the Netherlands. Violence is a prominent risk in acute psychiatry, and professional sensemaking, corrective measures (such as patient isolation (...) and sedation), and quantification instruments (such as the Brøset Violence Checklist, henceforth BVC) have previously been developed to deal with it. We juxtapose the different ways in which psychiatric nurses, the BVC, and algorithmic scores navigate assessments of the potential of future inpatient violence. We find that nurses approach violence assessment with an attitude of doubt and precaution: they aim to understand warning signs and probe alternative explanations to them, so as not to punish patients when not necessary. Being in charge of quantitative capture, they incorporate this attitude of doubt in the BVC scores. Conversely, the algorithmic risk scores import a logic of pre-emption into the clinic: they attempt to flag targets before warning signs manifests and are noticed by nurses. Pre-emption translates into punitive attitudes towards patients, to which nurses refuse to subscribe. During the pilots, nurses solely engage with algorithmic scores by attempting to reinstate doubt in them. We argue that pre-emption can hardly be incorporated into professional decision-making without importing punitive attitudes. As such, algorithmic outputs targeting ethically laden instances of decision-making are a cause for academic and political concern. (shrink)

The aim of this article is to explore the complex forms of knowledge involved in diagnostic and interventional decision making by comparing the processes in medicine and nursing, including nurse practitioners. Many authors assert that the practice of clinical decision making involves the application of theoretical knowledge (acquired in the classroom and textbooks) as well as research evidence, upon concrete particular cases. This approach draws on various universal principles and algorithms to facilitate the task. On the other hand, (...) others argue that this involves an intuitive form of judgement that is difficult to teach, one that is acquired principally through experience. In an exploration of these issues, this article consists of three sections. A clarification of terms commonly used when discussing decision making is provided in the first section. In the second section, an epistemological analysis of decision making is presented by examining several perspectives and comparing them for their use in the nursing and medical literature. Bunge's epistemological framework for decision making (based on scientific realism) is explored for its fit with the aims of medicine and nursing. The final section presents a discussion of knowledge utilization and decision making as it relates to the implications for the education and ongoing development of nurse practitioners. It is concluded that Donald Schön's conception of reflective practice best characterizes the skilful conduct of clinical decision making. (shrink)

Machine learning and other forms of artificial intelligence can improve parts of clinical decision making regarding the gathering and analysis of data, the detection of disease, and the provis...

Allowing algorithms to guide or determine decision-making in ethically complex situations, and eventually satisfying the need for good clinical ethics consultation work, is a philosophically intere...

Ataxia is a kind of external characteristics when the human body has poor coordination and balance disorder, it often indicates diseases in certain parts of the body. Many internal factors may causing ataxia; currently, observed external characteristics, combined with Doctor’s personal clinical experience play main roles in diagnosing ataxia. In this situation, different kinds of diseases may be confused, leading to the delay in treatment and recovery. Modern high precision medical instruments would provide better accuracy but the economic cost (...) is a non-negligible factor. In this paper, novel non-contact sensing technique is used to detect and distinguish sensory ataxia and cerebellar ataxia. Firstly, Romberg’s test and gait analysis data are collected by the microwave sensing platform; then, after some preprocessing, some machine learning approaches have been applied to train the models. For Romberg’s test, time domain features are considered, the accuracy of all the three algorithms are higher than 96%; for gait detection, Principal Component Analysis is used for dimensionality reduction, and the accuracies of Back Propagation neural Network, Support Vector Machine, and Random Forest are 97.8, 98.9, and 91.1%, respectively. (shrink)

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed (...) for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps. (shrink)

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study (...) is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery. (shrink)

Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and (...) erroneous treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine. (shrink)

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when (...) underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries. (shrink)

Situations in which patients lack medical decision-making (MDM) capacity raise ethical challenges, especially when the patients decline care that their surrogate decision makers and/or clinicians agree is indicated. These patients are a vulnerable population and should receive treatment that is the standard of care, in line with their the values of their authentic self, just as any other patient would. But forcing treatment on patients who refuse it, even though they lack capacity, carries medical and psychological risks to the patients (...) and the hospital staff. It is also often impractical to force some treatments, especially in the long term. For example, independent of the ethical “should” question, how would one force hemodialysis for the rest of a patient’s life, or force a surgery that requires weeks of post-operative physical therapy? In this article we present a novel algorithm that can help clinicians with ethical and practical decision making, with the goals of achieving the best outcomes for patients and reducing moral distress for their caretakers and clinicians. (shrink)

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)

Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked (...) to multiple clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed. (shrink)

Although there is wide agreement that ethics consults are at risk for conflicts of interest, ethics consultants have limited guidance with regard to how to identify and approach COIs. We aim to address these concerns and provide practical guidance. We will define and consider four categories of COIs: consult type, team composition, dual clinical roles and other concerns. We will define and consider six actions available for ECs to take in response to COIs: no action, disclosure only, obtaining a (...) second opinion, referring to another EC, referring to an institutional ethics committee or seeking an outside consult. We will then propose a points-based algorithm for ECs to use to determine the appropriate response to COI. Finally, we will discuss the strengths and limitations of our proposed algorithm. (shrink)

This article reads the uptake of facial-matching algorithms by fertility clinics in Spain through the lens of ‘the fertility fix’: a software fix to the social reconfiguration of kinship and a fixed capital investment made by competing fertility companies and firms. ‘The fertility fix’ is proposed as a critical, ethical lens through which to situate algorithmic facial-matching in assisted reproduction in the context of the racial politics of the face and phenotype and the spatial politics of market expansion. While (...) an ‘infertility crisis’ is often mentioned when explaining the growth of the assisted reproductive technologies (ARTs) industry, the use of donated reproductive cells is tied up in societal, ecological and economic shifts. Combining Software Studies analysis with Marxist Feminist and trans*feminist perspectives on shifting re/production dynamics, the article details the role of computational technologies in promoting certain ideas and beliefs about family and fixing certain territories of capital flow. (shrink)

Bioethics, Volume 36, Issue 2, Page 134-142, February 2022.

This article reads the uptake of facial-matching algorithms by fertility clinics in Spain through the lens of ‘the fertility fix’: a software fix to the social reconfiguration of kinship and a fixed capital investment made by competing fertility companies and firms. ‘The fertility fix’ is proposed as a critical, ethical lens through which to situate algorithmic facial-matching in assisted reproduction in the context of the racial politics of the face and phenotype and the spatial politics of market expansion. While (...) an ‘infertility crisis’ is often mentioned when explaining the growth of the assisted reproductive technologies (ARTs) industry, the use of donated reproductive cells is tied up in societal, ecological and economic shifts. Combining Software Studies analysis with Marxist Feminist and trans*feminist perspectives on shifting re/production dynamics, the article details the role of computational technologies in promoting certain ideas and beliefs about family and fixing certain territories of capital flow. (shrink)

The application of machine learning and artificial intelligence in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five (...) articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle. We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process. A third of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process. (shrink)

McCradden et al. propose to close the “AI chasm” between algorithms and clinically meaningful application using the norms of evidence-based medicine and clinical research, with the rat...

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG (...) epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies. (shrink)

This article analyzes local algorithmic practices resulting from the increased use of time-lapse (TL) imaging in fertility treatment. The data produced by TL technologies are expected to help professionals pick the best embryo for implantation. The emergence of TL has been characterized by promissory discourses of deeper embryo knowledge and expanded selection standardization, despite professionals having no conclusive evidence that TL improves pregnancy rates. Our research explores the use of TL tools in embryology labs. We pay special attention to standardization (...) efforts and knowledge-creation facilitated through TL and its incorporated algorithms. Using ethnographic data from five UK clinical sites, we argue that knowledge generated through TL is contingent upon complex human–machine interactions that produce local uncertainties. Thus, algorithms do not simply add medical knowledge. Rather, they rearrange professional practice and expertise. Firstly, we show how TL changes lab routines and training needs. Secondly, we show that the human input TL requires renders the algorithm itself an uncertain and situated practice. This, in turn, raises professional questions about the algorithm’s authority in embryo selection. The article demonstrates the embedded nature of algorithmic knowledge production, thus pointing to the need for STS scholarship to further explore the locality of algorithms and AI. (shrink)

Background: Empirical ethics inquiry works from the notion that stakeholder perspectives are necessary for gauging the ethical acceptability of human studies and assuring that research aligns with societal expectations. Although common, studies involving different populations often entail comparisons of trends that problematize the interpretation of results. Using graphical model selection—a technique aimed at transcending limitations of conventional methods—this report presents data on the ethics of clinical research with two objectives: (1) to display the patterns of views held by ill (...) and healthy individuals in clinical research as a test of the study's original hypothesis and (2) to introduce graphical model selection as a key analytic tool for ethics research. Methods: In this institutional review board (IRB)-approved, National Institutes of Health-funded project, data were collected from 60 mentally ill and 43 physically ill clinical research protocol volunteers, 47 healthy protocol-consented participants, and 29 healthy individuals without research protocol experience. Respondents were queried on the ethical acceptability of research involving people with mental and physical illness (i.e., cancer, HIV, depression, schizophrenia, and posttraumatic stress disorder) and non-illness-related sources of vulnerability (e.g., age, class, gender, ethnicity). Using a statistical algorithm, we selected graphical models to display interrelationships among responses to questions. Results: Both mentally and physically ill protocol volunteers revealed a high degree of connectivity among ethically salient perspectives. Healthy participants, irrespective of research protocol experience, revealed patterns of views that were not highly connected. Conclusions: Between ill and healthy protocol participants, the pattern of views was vastly different. Experience with illness was tied to dense connectivity, whereas healthy individuals expressed views with sparse connections. In offering a nuanced perspective on the interrelation of ethically relevant responses, graphical model selection has the potential to bring new insights to the field of ethics. (shrink)

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based (...) on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods. (shrink)

Recent developments in pattern analysis research have made this methodology suitable for the study of the processes that are set in motion in psychological interventions. Outcome research, based on the comparison between clinical results from treatment and control groups, has leveraged our empirical knowledge about the efficacy of psychological interventions. However, these methods of research are not precise enough for the analysis of these processes. On the contrary, pattern analysis could be a powerful tool to study moment-to-moment interactions typical (...) of psychological interventions. This is methodology is relevant because clinical psychology is experiencing a paradigm shift from a protocol for syndrome perspective to a principle-based and person-centered intervention. This evidence-based, theory-grounded, and process-oriented paradigm of clinical intervention needs new research methods to thrive. The analysis of the therapeutic relationship built into the verbal interaction between the clinician and the client is one of the cornerstones of this new era of research. So, the purpose of this article is three-fold: to discuss the role of the verbal interaction pattern analysis in the clinical context to the development of the principle-based clinical psychology, to analyze the patterns of verbal interaction in a clinical case, and to compare the results using two different methods. To reach these purposes, using the observational methodology, we have coded the verbal interaction of 16 clinical sessions with a person diagnosed with a borderline personality disorder. We have analyzed the data using sequential analysis and pattern recognition algorithms. We have been able to detect typical patterns during different phases of psychological intervention. Finally, the conceptual, methodological, and empirical implications of this study will be discussed within the realms of pattern analysis research and principle-based clinical psychology. (shrink)

This paper analyzes the collaboration between healthcare professionals and algorithms in making decisions within the realm of public healthcare. By extending the concept of ‘tinkering’ from previous research conducted by philosopher Mol (Care in practice. On tinkering in clinics, homes and farms Verlag, Amsterdam, 2010) and anthropologist Pols (Health Care Anal 18: 374–388, 2009), who highlighted the improvisational and adaptive practices of healthcare professionals, this paper reveals that in the context of digitalizing healthcare, both professionals and algorithms engage (...) in what I call ‘collaborative tinkering’ as they navigate the intricate and unpredictable nature of healthcare situations together. The paper draws upon an idea that is increasingly common in academic literature, namely that healthcare professionals and the algorithms they use can form a hybrid decision-making entity, challenging the conventional notion of agency and intelligence as being exclusively confined to individual humans or machines. Drawing upon an international, ethnographic study conducted in different hospitals around the world, the paper describes empirically how humans and algorithms come to decisions together, making explicit how, in the practice of daily work, agency and intelligence are distributed among a range of actors, including humans, technologies, knowledge resources, and the spaces where they interact. The concept of collaborative tinkering helps to make explicit how both healthcare professionals and algorithms engage in adaptive improvisation. This exploration not only enriches the understanding of collaborative dynamics between humans and AI but also problematizes the individualistic conception of AI that still exists in regulatory frameworks. By introducing empirical specificity through ethnographic insights and employing an anthropological perspective, the paper calls for a critical reassessment of current ethical and policy frameworks governing human–AI collaboration in healthcare, thereby illuminating direct implications for the future of AI ethics in medical practice. (shrink)