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.

Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have (...) proposed a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. (...) Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

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)

This essay discusses critical approaches to culture, difference, and empathy in health care education through a reading of Junot Diaz’s “Wildwood” chapter from the 2007 novel The Brief Wondrous Life of Oscar Wao. I begin with an analysis of the way that Diaz’s narrative invites readers to imagine and explore the experiences of others with subtlety and complexity. My reading of “Wildwood” illuminates its double-edged injunction to try to imagine another’s perspective while recognizing the limits to—or even the impossibility of—that (...) exercise. I draw on post-colonial theory and feminist science studies to illuminate a text that is created and interpreted in a post-colonial context—the Dominican diaspora in the United States. The essay offers a model of historical and critical analysis that health care educators can use to frame the concept of empathy in the classroom and the clinic. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical (...) image classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized (...) for earlier evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the underrsampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the (...) patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with (...) backgrounds in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision-making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub-kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for (...) this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of (...) CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

Context — Children and adults with psychopathic traits and conduct or oppositional defiant disorder demonstrate poor decision making and are impaired in reversal learning. However, the neural basis of this impairment has not previously been investigated. Furthermore, despite high comorbidity of psychopathic traits and attention deficit/hyperactivity disorder, to our knowledge, no research has attempted to distinguish neural correlates of childhood psychopathic traits and attention-deficit/hyperactivity disorder. Objective—To determine the neural regions that underlie the reversal learning impairments in children with (...) psychopathic traits plus conduct or oppositional defiant disorder. Design — Case-control study. Setting — Government clinical research institute. Participants — Forty-two adolescents aged 10 to 17 years: 14 with psychopathic traits and oppositional defiant disorder or conduct disorder, 14 with attention-deficit/hyperactivity disorder only, and 14 healthy controls. Main Outcome Measure — Blood oxygenation level–dependent signal as measured via functional magnetic resonance imaging during a probabilistic reversal task. Results — Children with psychopathic traits showed abnormal responses within the ventromedial prefrontal cortex (Brodmann area 10) during punished reversal errors compared with children wit hattention deficit/hyperactivity disorder and healthy children (P < .05 corrected for multiple comparisons). Conclusions — To our knowledge, this study provides the first evidence of abnormal ventromedial prefrontal cortex responsiveness in children with psychopathic traits and demonstrates this dysfunction was not attributable to comorbid attention-deficit/hyperactivity disorder. These findings suggest that reversal learning impairments in patients with developmental psychopathic traits relate to abnormal processing of reinforcement information. (shrink)

Vast archives of fragmentary structural brain scans that are routinely acquired in medical clinics for diagnostic purposes have so far been considered to be unusable for neuroscientific research. Yet, recent studies have proposed that by deploying machine learning algorithms to fill in the missing anatomy, clinical scans could, in future, be used by researchers to gain new insights into various brain disorders. This chapter focuses on a study published in2019, whose authors developed a novel unsupervised machine learning (...) algorithm for synthesising missing anatomy in extremely sparse clinical MRI scans of thousands of stroke patients. By approaching the study from a media-theoretical perspective, I analyse how its authors dis-cursively negotiated the anatomical and operative plausibility of the unobserved anatomy that their black-boxed algorithm reconstructed from the existing sparse data. My analysis fore-grounds the processual, relational, context-dependent, and essentially unstable character of the thus established plausibility of the algorithmically synthesised neuroanatomy. (shrink)

The three learning modalities in nursing education are classroom meetings, skill laboratory practices, and clinical practice in hospital or community settings. In clinical internships, the collaborative self-directed learning method is highly encouraged among nursing students. The use of information and communication technologies (ICT) in clinical learning supports the implementation of evidence-based nursing and student-centered learning. The current study examines whether the relationship between social influence and ICT using intention is moderated by the daily (...) duration of use and the number of classmates being friends on social media. The Bayesian Mindsponge Framework (BMF) analytics was employed on a dataset of 702 Moroccan nursing students. We found that social influence was positively associated with nursing students’ intention to use ICT for clinical learning during the internship. This positive association was positively moderated by the daily duration of social media usage and negatively moderated by the number of classmates being friends on social media. These findings underscore the potential of using social media platforms to facilitate the incorporation of ICT and social media in nursing education and training. However, the adverse effects of social media on students’ mental well-being also need to be acknowledged and managed appropriately. (shrink)

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available (...) from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use. (shrink)

Clinical practice is a part of the integral learning method in nursing education. The use of information and communication technologies (ICT) in clinical learning is highly encouraged among nursing students to support evidence-based nursing and student-centered learning. Through the information-processing lens of the mindsponge theory, this study views performance expectancy (or perceived usefulness) and effort expectancy (or perceived ease of use) as results of subjective benefit and cost judgments determining the students’ ICT using intention for (...) supporting clinical learning, respectively. Therefore, the study examines whether effort expectancy moderates the relationship between performance expectancy and the intention to use ICT among Moroccan nursing students. The Bayesian Mindsponge Framework (BMF) analytics was employed on a dataset of 702 Moroccan nursing students. We found that nursing students’ performance expectancy is positively associated with the intention to adopt ICT and social media, while effort expectancy positively moderates this relationship. Moreover, the model with effort expectancy as the moderator was discovered to have a significantly larger weight than the model with effort expectancy as the direct predictor, validating our information-processing reasoning. This study provides a new perspective on the interplay between the benefit and cost perceptions of ICT use to support clinical learning methods in nursing education. Moreover, given the limited education resources, especially in developing countries like Morocco, we recommend nursing education and training prioritize communicating the benefits of ICTs and social media over ease of use. Doing so would help improve the effectiveness and efficiency of nursing education and training while lowering costs. (shrink)

Placebos are often used by clinicians, usually deceptively and with little rationale or evidence of benefit, making their use ethically problematic. In contrast with their typical current use, a provocative line of research suggests that placebos can be intentionally exploited to extend analgesic therapeutic effects. Is it possible to extend the effects of drug treatments by interspersing placebos? We reviewed a database of placebo studies, searching for studies that indicate that placebos given after repeated administration of active treatments acquire medication-like (...) effects. We found a total of 22studies in both animals and humans hinting of evidence that placebos may work as a sort of dose extender of active painkillers. Wherever effective in relieving clinical pain, such placebo use would offer several advantages. First, extending the effects of a painkiller through the use of placebos may reduce total drug intake and side effects. Second, dose-extending placebos may decrease patient dependence. Third, using placebos along with active medication, for part of the course of treatment, should limit dose escalation and lower costs. Importantly, provided that nondisclosure is pre-authorized in the informed consent process and that robust evidence indicates therapeutic benefit comparable to that of standard full-dose therapeutic regimens, introducing dose-extending placebos into the clinical arsenal should be considered. This novel prospect of placebo use has the potential to change our general thinking about painkiller treatments, the typical regimens of painkiller applications, and the ways in which treatments are evaluated. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI (...) safety. (shrink)

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)

Clinical evidence has shown that physical exercise during pregnancy may alter brain devel- opment and improve cognitive function of offspring. However, the mechanisms through which maternal exercise might promote such effects are not well understood. The present study examined levels of brain-derived neurotrophic factor (BDNF) and absolute cell num- bers in the hippocampal formation and cerebral cortex of rat pups born from mothers exer- cised during pregnancy. Additionally, we evaluated the cognitive abilities of adult offspring in different behavioral paradigms (...) (exploratory activity and habituation in open field tests, spatial memory in a water maze test, and aversive memory in a step-down inhibitory avoid- ance task). Results showed that maternal exercise during pregnancy increased BDNF lev- els and absolute numbers of neuronal and non-neuronal cells in the hippocampal formation of offspring. No differences in BDNF levels or cell numbers were detected in the cerebral cortex. It was also observed that offspring from exercised mothers exhibited better cognitive performance in nonassociative (habituation) and associative (spatial learning) mnemonic tasks than did offspring from sedentary mothers. Our findings indicate that maternal exer- cise during pregnancy enhances offspring cognitive function (habituation behavior and spa- tial learning) and increases BDNF levels and cell numbers in the hippocampal formation of offspring. (shrink)

In this paper, I examine Reinforcement Learning modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang’s account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

In domains like stock brokerage, clinical psychiatry, and long‐term political forecasting, experts generally fail to outperform novices. Empirical researchers agree on why this is: experts must receive direct or environmental learning feedback during training to develop reliable expertise, and these domains are deficient in this type of feedback. A growing number of philosophers resource this consensus view to argue that, given the absence of direct or environmental philosophical feedback, we should not give the philosophical intuitions or theories of (...) expert philosophers greater credence than those of novice philosophers. This article has three objectives. The first is to explore several overlooked issues concerning the strategy of generalizing from empirical studies of non‐philosophical expertise to the epistemic status of philosophical expertise. The second is to explain why empirical research into a causal relationship between direct learning feedback and enhanced expert performance does not provide good grounds for abandoning a default optimism about the epistemic superiority of expert philosophical theories. The third is to sketch a positive characterization of learning feedback that addresses developmental concerns made salient by the empirical literature on expert performance for specifically theory‐driven or “armchair” domains like philosophy. (shrink)

School administrators are mandated to take the instructional leadership roles. On this premise, a study assessed the extent of instructional leadership practices of public elementary school administrators in El Salvador City Division, Philippines. Also, it explored their actual practices, challenges encountered, and the ways they overcome the challenges in practicing instructional leadership. It employed a mixed-method research design. It administered the adopted assessment tool on instructional leadership to 15 school administrators and 12 of them were involved in the individual interviews. (...) This was conducted between the last quarter of 2019 and the first quarter of 2020. Descriptive statistics were used to describe the extent of instructional leadership practices. Also, it analyzed the actual practices, the challenges encountered, and the ways of overcoming these challenges using thematic narrative analysis. Results revealed that public school administrators have always practiced the four domains or strands of instructional leadership to a very high extent. Providing the technical assistance, conducting clinical supervision, and innovating teaching and learning emerged as themes of their actual practices. These administrators had encountered challenges in dealing with teachers’ attitudes, conflicting schedules and activities, and teachers’ resistance to changes. They overcome the challenges by trying to meet the competency standards, adapting and modifying the existing programs, contextualizing teaching and learning, and inculcating the value and benefits of class observation. Looking at the findings from the lens of deliberate practice theory, it was concluded that school administrators have indicated they have acquired knowledge and a high level of understanding of their instructional leadership roles. But despite of this, they still met challenges and have tried their best to manage them. This study presented some doable and practical recommendations to the Department of Education (DepEd) and concerned offices which may benefit both the internal and external stakeholders of the schools. (shrink)

Common mental health disorders are rising globally, creating a strain on public healthcare systems. This has led to a renewed interest in the role that digital technologies may have for improving mental health outcomes. One result of this interest is the development and use of artificial intelligence for assessing, diagnosing, and treating mental health issues, which we refer to as ‘digital psychiatry’. This article focuses on the increasing use of digital psychiatry outside of clinical settings, in the following sectors: (...) education, employment, financial services, social media, and the digital well-being industry. We analyse the ethical risks of deploying digital psychiatry in these sectors, emphasising key problems and opportunities for public health, and offer recommendations for protecting and promoting public health and well-being in information societies. (shrink)

The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because (...) it helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources. (shrink)

Philosophers of science and medicine now aspire to provide useful, socially relevant accounts of mechanism. Existing accounts have forged the path by attending to mechanisms in historical context, scientific practice, the special sciences, and policy. Yet, their primary focus has been on more proximate issues related to therapeutic effectiveness. To take the next step toward social relevance, we must investigate the challenges facing researchers, clinicians, and policy makers involving values and social context. Accordingly, we learn valuable lessons about the connections (...) between mechanistic processes and more fundamental reasons for medical interventions, particularly moral, ethical, religious, and political concerns about health, agency, and power. This paper uses debates over the controversial morning-after pill to gain insight into the deeper reasons for the production and use of mechanistic knowledge throughout biomedical research, clinical practice, and governmental regulation. To practice socially relevant philosophy of science, I argue that we need to account for mechanistic knowledge beyond immediate effectiveness, such as how it can also provide moral guidance, aid ethical categorization in the clinic, and function as a political instrument. Such insights have implications for medical epistemology, including the value-laden dimensions of mechanistic reasoning and the “epistemic friction” of values. Furthermore, there are broader impacts for teaching research ethics and understanding the role of science advisors as political advocates. (shrink)

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these (...) concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. (shrink)

Substance-abusing women are vulnerable to specific kinds of epistemic injustice, including stigmatization and discrimination. This article examines the development of the epistemic agency of female substance abusers by asking: How does the use of a formal discussion protocol in community rehabilitation interaction alleviate epistemic injustice and strengthen the epistemic agency of substance abusers? The data were collected in a Finnish rehabilitation center by videotaping six group discussions between social workers, peer support workers, and rehabilitation clients with substance abuse problems. Of (...) these data, one recorded group discussion between four female participants—two rehabilitation clients, a peer support worker, and a social adviser—was used in this paper. Using conversational analysis, the findings indicate that, through the collaborative activities of sharing experiential knowledge about substance abuse and discussing the experiences of abuse in the rehabilitation interaction, substance abusers can develop novel ways to strengthen their epistemic agency by enhanced self-awareness. The discussion protocol is an epistemic tool that professionals and clients can learn to use in ethically and epistemologically successful ways in interaction. The use of a discussion protocol is an example of social professionals’ clinical knowledge of intensifying collaboration and sharing experiential knowledge in community rehabilitation and other substance abuse services. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research (...) institutions, and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

Deep brain stimulation (DBS), a surgical procedure involving the implantation of electrodes in the brain, has rekindled the medical community’s interest in psychosurgery. Whereas many researchers argue DBS is substantially different from psychosurgery, we argue psychiatric DBS—though a much more precise and refined treatment than its predecessors—is nevertheless a form of psychosurgery, which raises both old and new ethical and legal concerns that have not been given proper attention. Learning from the ethical and regulatory failures of older forms of (...) psychosurgery can help shed light on how to address the regulatory gaps that exist currently in DBS research. To show why it is important to address the current regulatory gaps within psychiatric DBS, we draw on the motivations underlying the regulation of earlier forms of psychosurgery in the US. We begin by providing a brief history of psychosurgery and electrical brain stimulation in the US. Against this backdrop, we introduce psychiatric DBS, exploring current research and ongoing clinical trials. We then draw out the ethical and regulatory similarities between earlier forms of psychosurgery and psychiatric DBS. As we will show, the factors that motivated strict regulation of earlier psychosurgical procedures mirror concerns with psychiatric DBS today. We offer three recommendations for psychiatric DBS regulation, which echo earlier motivations for regulating psychosurgery, along with new considerations that reflect the novel technologies used in DBS. (shrink)

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on (...) the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted. (shrink)

Except in extraordinary circumstances, patients' clinical care should reflect their preferences. Incapacitated patients cannot report their preferences. This is a problem. Extant solutions to the problem are inadequate: surrogates are unreliable, and advance directives are uncommon. In response, some authors have suggested developing algorithmic "patient preference predictors" (PPPs) to inform care for incapacitated patients. In a recent paper, Earp et al. propose a new twist on PPPs. Earp et al. suggest we personalize PPPs using modern machine learning (ML) (...) techniques. In this paper, I argue that, contrary to Earp et al.'s claim, personalized patient preference predictors are neither technically feasible nor ethically desirable. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value of (...) interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realised. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems , may itself constitute a form of lethal prejudice that may diminish the benefits of AI to - and perhaps even harm - patients. (shrink)

A newborn baby is an infant within the first 28 days of birth. Diagnosis and treatment of infant diseases require specialized medical resources and expert knowledge. However, there is a shortage of such professionals globally, particularly in low-income countries. To address this challenge, a knowledge-based system was designed to aid in the diagnosis and treatment of neonatal diseases. The system utilizes both machine learning and health expert knowledge, and a hybrid data mining process model was used to extract knowledge (...) from a clinical dataset. The PART algorithm achieved the highest performance result with 98.06% accuracy under 10-fold cross-validation, and the generated rules were used to develop the knowledge-based system. The system achieved 90.9% accuracy in system performance testing and 89.2% in user acceptance testing, and is intended to serve as an assistant tool for healthcare experts. (shrink)

Psychopathy is increasingly in the public eye. However, it is yet to be fully and effectively understood. Within the context of the DSM-IV, for example, it is best regarded as a complex family of disorders. The upside is that this family can be tightly related along common dimensions. Characteristic marks of psychopaths include a lack of guilt and remorse for paradigm case immoral actions, leading to the common conception of psychopathy rooted in affective dysfunctions. An adequate portrait of psychopathy is (...) much more complicated, however. Though some neural regions and corresponding functions are commonly indicated, they range across those responsible for action planning and learning, as well as emotional processes. Accordingly, a complete fine-grained map of all neural mechanisms responsible for psychopathy has not been realized, and even if it were, such a map would have limited utility outside of the context of surgical or chemical intervention. The utility of a neural-level understanding of psychopathy is further limited by the fact that it is only applicable in the clinical identification of individual subjects, and the neuro-chemical/biological correction of those subjects after they are positively identified as psychopaths. On the other hand, an information processing model of moral cognition provides for wider-ranging applications. The theoretical and practical implications for such a feasible working model of psychopathic personalities are assessed. Finally, this chapter raises the possibility of directed modification of social-environmental factors discouraging the development of psychopathic personalities in the first place, modifications which are also open to simulation and testing in terms of the same model of moral cognition. (shrink)

Many students consider mathematics as the most dreaded subject in their curriculum, so much so that the term “math phobia” or “math anxiety” is practically a part of clinical psychological literature. This symptom is widespread and students suffer mental disturbances when facing mathematical activity because understanding mathematics is a great task for them. This paper described the students’ cognitive skills performance in Basic Mathematics based on the following logical operations: Classification, Seriation, Logical Multiplication, Compensation, Ratio and Proportional Thinking, Probability (...) Thinking and Correlation Thinking as it serves a critical element of teaching and learning, that is determining the current position of the learners’ mathematical capacity. Its implications for mathematics education were also revealed. A descriptive quantitative design was used in this study. The study included 1011 first-year college students from six state universities in the Philippines who were enrolled in the Bachelor of Science of Secondary Education Program during the first semester of the Academic Year 2019-2020. This study made use of the teacher-made test called the Test on Logical Operations. Findings revealed that the cognitive skills achievement of the first year BSE students from different state universities in the Philippines falls under the category of late concrete operational stage. As a result, students are unable to perform the logical operational skills expected of their age. At their age level, they are expected to be under the formal operational stage based on Piaget’s stages of cognitive development. These findings revealed a weak mathematical education foundation among students which requires immediate attention. This reality should be recognized by educational planners and implementers when making curricular and other instructional decisions. (shrink)

In this chapter, data concerning the development of principal aspects of vision is reviewed. First, the development of colour vision and luminance perception is discussed. Relevant data accumulated so far indicates that perception of colour and luminance is present by 6-9 months of age. The presence of typical color illusions at this age suggests that the phenomenal character of color experience is comparable to that of adults well before the first birthday. Thus it seems plausible that color perception develops on (...) the grounds of genetically preprogrammed maturation, in which perceptual learning and plasticity play a limited role. This claim also seems to be supported by case studies of newly sighted patients. On the other hand, perceptual integration of edges and forms in the visual environment as well as the development of binocular vision, and the perception of ambiguous figures seem to depend crucially on early perceptual learning. Both developmental and clinical data indicate a more significant involvement of perceptual learning in such mid-level and higher level perceptual phenomena. (shrink)

In using brain stimulation technology to suppress an individual’s fear response to a traumatic memory, we are effectively altering that individual’s identity. In this article, I argue that until we learn more, such technology should be available only to patients with objectively debilitating fear responses who give their informed consent. First, I provide an overview of how the technology works. Second, I analyze the artificial, natural, and clinical changes in memory, and explores the ethical concerns associated with altering an (...) individual’s identity. I conclude by asserting that the benefits of brain stimulation technology outweigh the concerns if the proper administrative procedure is followed. (shrink)

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role (...) of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML. (shrink)

David Tomasi presents new, groundbreaking research on the science and application of Mind-Body Medicine strategies to improve clinical outcomes in inpatient psychiatry settings. Much more than a list of therapeutic recommendations, this book is a thorough description of how Mind-Body Medicine can be successfully applied, from a therapeutic as well as from an organizational, cost-effective analysis viewpoint, to the full spectrum of psychiatric treatments. Furthermore, this study examines the role of multidisciplinary and interdisciplinary treatment teams, with a special focus (...) on the profession and the role of psychotherapists and group therapists, thereby providing solid scientific evidence of the benefits of patient-provider therapeutic alliances. In this sense, this book serves as a guide for professionals and institutions both in the private and the public sphere, to learn effective treatment and management strategies. About the Author Psychotherapist, Researcher, and Philosopher Dr. David Låg Tomasi is the author of Medical Philosophy (ibidem, 2016) and the co-author of Positive Patient Response to a Structured Exercise Program Delivered in Inpatient Psychiatry (Global Advances in Health and Medicine, 2019), the most-read research item from the University of Vermont (Researchgate, 2019). A member of several national and international Academies of Sciences, Dr. Tomasi works in the Inpatient Psychiatry Unit at the UVM Medical Center, and teaches at the University of Vermont and the Community College of Vermont. ---- Ibidem / Columbia University Press. (shrink)

At the age of 70 years, Professor Chris Nwamuo is still breaking new grounds in the Theatre, Media and Communication disciplines, not only in the University of Calabar, but also in Cross River University of Technology (CRUTECH) in Cross River State Nigeria, Abia State University in Abia State, Nigeria and many other state, national and international higher institutions of learning. He is tireless in research, clinical in project supervision, stern in the resolution of academic knots and committed to (...) teaching students both at the graduate and undergraduate levels. One would imagine that despite the ever busy schedule of this prolific scholar, including his attendance to family affairs, he still has the time to write out his findings, thoughts and perspectives in the very field that he has worked for more than three decades. One of his most recent works is his book “The Dynamics of International Communication” published in 2016. Communication is the human nutrient that grows a healthy relationship among people and nations of the world. When absent or not properly handled, communication effects result to rumor, grapevines, conflict or in internationally severe cases, war. ‘Communication’ as a term is surrounded by mysteries of comprehension but Nwamuo has attempted an unraveling throughout his years of academic surgeon in the field. Ike (2005:40) define communication as the process by which people share information, ideas and feelings using spoken and written words, body language, personal mannerism and style, the physical environment or anything that adds meaning to a message. Iyorza (2015:11) views communication as the transmission of information, idea or beliefs from the encoder to the decoder with the aim of achieving a common ground of understanding, promoting a course or reacting to given stimuli in the communication encounter. Both definitions share a common characteristic: information or ideas sharing or transmission. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a (...) Logistic relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

The development of a book is an enjoyable task. Whatever the contents of this book might be, I assure you that I would try my best to put things in such a simple language in an easy-to-understand manner. Moreover, I also promise to be as blunt and frank as I could be. -/- I have been thinking of writing this book for quite some time; however, I have decided if I am going to write one book, then I might as (...) well write more books since I am going over the basics of some of my thoughts and theories in this very first book.6 However, my thoughts and theories are the least important since everybody could be wrong, and my thoughts could have been the same as others. -/- Yet, as mentioned earlier, these are only the base of my thoughts, observations, and theories and do not include all the subjects I would love to share. I have written in this very first book a brief introduction to some of my thoughts. -/- Furthermore, they are nothing special since we all think we have something special to say. I might perhaps be under the same impression as everybody else. -/- Also, I have to admit that I am an average person like any other person who could make mistakes. Therefore, I see myself entitled to change of opinion if better logic is provided sufficiently. -/- I have to try writing this book since it is already long overdue, and it is time to share my opinions and7 understandings of the world as simple and as naive that they can be! -/- Moreover, this book will be as I usually say that ''in school, first, you learn a lesson then you have a test, but in life, first, you have a test then you learn a lesson''. -/- So I promise to listen to your criticism and pieces of advice to make myself better and develop into a better and wiser individual. I hope this book helps us both. -/- This book is not about me being right or wrong since I will always look forward to criticism since critics are the ones that help us be better at what we are or what we are trying to accomplish. (shrink)

Psychiatry has witnessed a new wave of approaches to clinical phenotyping and the study of psychopathology, including the National Institute of Mental Health’s Research Domain Criteria, clinical staging, network approaches, the Hierarchical Taxonomy of Psychopathology, and the general psychopathology factor, as well as a revival of interest in phenomenological psychopathology. The question naturally emerges as to what the relationship between these new approaches is – are they mutually exclusive, competing approaches, or can they be integrated in some way (...) and used to enrich each other? In this opinion piece, we propose a possible integration between clinical staging and phenomenological psychopathology. Domains identified in phenomenological psychopathology, such as selfhood, embodiment, affectivity, etc., can be overlaid on clinical stages in order to enrich and deepen the phenotypes captured in clinical staging (‘high resolution’ clinical phenotypes). This approach may be useful both ideographically and nomothetically, in that it could complement diagnosis, enrich clinical formulation, and inform treatment of individual patients, as well as help guide aetiological, prediction, and treatment research. The overlaying of phenomenological domains on clinical stages may require that these domains are reformulated in dimensional rather than categorial terms. This integrative project requires assessment tools, some of which are already available, that are sensitive and thorough enough to pick up on the range of relevant psychopathology. The proposed approach offers opportunities for mutual enrichment: clinical staging may be enriched by introducing greater depth to phenotypes; phenomenological psychopathology may be enriched by introducing stages of severity and disorder progression to phenomenological analysis. (shrink)

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

The desideratum of semantic interoperability has been intensively discussed in medical informatics circles in recent years. Originally, experts assumed that this issue could be sufficiently addressed by insisting simply on the application of shared clinical terminologies or clinical information models. However, the use of the term ‘ontology’ has been steadily increasing more recently. We discuss criteria for distinguishing clinical ontologies from clinical terminologies and information models. Then, we briefly present the role clinical ontologies play in (...) two multicentric research projects. Finally, we discuss the interactions between these different kinds of knowledge representation artifacts and the stakeholders involved in developing interoperational real-world clinical applications. We provide ontology engineering examples from two EU-funded projects. (shrink)

Outcomes research in healthcare has been a topic much addressed in recent years. Efforts in this direction have been supplemented by work in the areas of guidelines for clinical practice and computer-interpretable workflow and careflow models.In what follows we present the outlines of a framework for understanding the relations between organizations, guidelines, individual patients and patient-related functions. The derived framework provides a means to extract the knowledge contained in the guideline text at different granularities, in ways that can help (...) us to assign tasks within the healthcare organization and to assess clinical performance in realizing the guideline. It does this in a way that preserves the flexibility of the organization in the adoption of the guidelines. (shrink)