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As the use of Artificial Intelligence (AI) technologies in healthcare is expanding, patients in the European Union (EU) are increasingly subjected to automated medical decision-making. This development poses challenges to the protection of patients' rights. A specific patients' right not to be subject to automated medical decision-making is not considered part of the traditional portfolio of patients' rights. The EU AI Act also does not contain such a right. The General Data Protection Regulation (GDPR) does, however, provide for the right 'not to be subject to a decision based solely on automated processing' in Article 22. At the same time, this provision has been severely critiqued in legal scholarship because of its lack of practical effectiveness. However, in December 2023, the Court of Justice of the EU first provided an interpretation of this right in C-634/21 (SCHUFA)—although in the context of credit scoring. Against this background, this article provides a critical analysis of the application of Article 22 GDPR to the medical context. The objective is to evaluate whether Article 22 GDPR may provide patients with the right to refuse automated medical decision-making. It proposes a health-conformant reading to strengthen patients' rights in the EU. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of "keyword, publication year, and publication language". The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was "USA", the journal was "Journal of Medical Internet Research", the author was "Meng Ji", and the most cited author was "Weihua Li". The 55 most cited (≥50) articles focused on themes related to "diagnosis of COVID-19 disease", "diagnosis of diseases", "detection and classification of cancerous cells", "identification of disease risk factors and disease prediction", "prediction of treatment outcomes", "prediction of disease course", "personalized treatment recommendations", "decision-making processes", "ethical considerations, risks, and responsibilities". With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most. [ABSTRACT FROM AUTHOR]

Background: The rapid advent of artificial intelligence (AI) and machine learning (ML) in the healthcare sector offers new horizons for patient education and has the potential to redefine patient-provider interactions. Our research aimed to gauge the efficacy of ChatGPT-4 in delivering accurate, current, and safe medical guidance on breast reconstruction, and benchmark its responses against other established patient information channels. Methods: We presented ChatGPT-4 with six frequently posed questions about breast reconstruction. The model's replies were critically assessed by a committee of experienced plastic and reconstructive surgeons. To ensure the precision of the information, its responses were further cross-referenced against two major medical databases. Results: The results revealed that ChatGPT-4 produced well-articulated, factually sound, and holistic answers to the presented inquiries. However, the platform showed constraints in offering tailored guidance and occasionally cited outdated or irrelevant references. Notably, the system consistently advocated for professional consultation for nuanced information. Conclusion: ChatGPT-4 has emerged as a potential supplementary resource in patient education concerning breast reconstruction. Nevertheless, to harness its full potential and ensure its seamless integration into healthcare, further refinements and advancements in AI tools are paramount. The study underscores the importance of continuous evaluation and enhancement for AI solutions in the evolving landscape of patient education. [ABSTRACT FROM AUTHOR]

What does racial justice--and racial injustice--look like with respect to artificial intelligence in medicine ("medical AI")? This Article offers that racial injustice might look like a country in which law and ethics have decided that it is unnecessary to inform people of color that their health is being managed by a technology that likely encodes the centuries of inequitable medical care that people of color have received. Racial justice might look like an informed consent process that is reformed in light of this reality. This Article makes this argument in four Parts. Part I canvases the deep and wide literature that documents that people of color suffer higher rates of illness than their white counterparts while also suffering poorer health outcomes than their white counterparts when treated for these illnesses. Part II then provides an introduction to AI and explains the uses that scholars and developers predict medical AI technologies will have in healthcare, focusing specifically on the management of pregnancy. Part III subsequently serves as a primer on algorithmic bias--that is, systematic errors in the operation of an algorithm that result in a group being unfairly advantaged or disadvantaged. This Part argues that we should expect algorithmic bias that results in people of color receiving inferior pregnancy-related healthcare, and healthcare generally, because medical AI technologies will be developed, trained, and deployed in a country with striking and unforgivable racial disparities in health. Part IV forms the heart of the Article, making the claim that obstetricians, and healthcare providers generally, should disclose during the informed consent process their reliance on, or consultation with, medical AI technologies that likely encode inequities. To be precise, providers should have to tell their patients that an algorithm has informed the recommendation that the provider is making; moreover, providers should inform their patients how racial disparities in health may have impacted the algorithm's accuracy. It supports this argument by recounting the antiracist, anti-white supremacist--indeed radical--origins of informed consent in the Nuremberg Trials' rebuke of Nazi "medicine." This Part argues that the introduction into the clinical encounter of medical AI--and the likelihood that these technologies will perpetuate racially inequitable healthcare while masking the same--is an invitation to reform the informed consent process to make it more consistent with the commitments that spurred its origination. This Part proposes that, given the antiracist roots of the doctrine of informed consent, it would be incredibly ironic to allow the informed consent process to permit a patient--and, particularly, a patient of color--to remain ignorant of the fact that their medical care is being managed by a device or system that likely encodes racism. This Part argues that informing patients about the likelihood of race-based algorithmic bias--and the reasons that we might expect race-based algorithmic bias--may, in fact, be a prerequisite for actually transforming the inequitable social conditions that produce racial disparities in health and healthcare. [ABSTRACT FROM AUTHOR]

In an era marked by swift technological advancements, the integration of artificial intelligence (AI) into the sphere of medicine shines as a beacon of hope. This assimilation possesses the ability to generate a profound transformation, fundamentally altering the approach to healthcare. The concept of AI in medicine is not new. Still, advancements in computational proficiencies and data accessibility, linked with notable advancements in machine and deep learning algorithms, have created a vast array of opportunities for the future of healthcare. This Article examines the multifaceted influences of AI in the healthcare field, delving into its contributions to diagnostics, treatment courses, patient care, and medical research. AI is on the brink of reshaping the medical landscape and beginning a new period marked by personalized, data-driven medicine. However, this metamorphosis raises many unexplored legal, ethical, and regulatory issues that must be resolved. [ABSTRACT FROM AUTHOR]

The German Ethics Council has published recommendations for the ethically responsible use of Artificial Intelligence (AI) in medicine. The statement emphasizes the need for a comprehensive examination of the ethical aspects of AI and new technologies. Various applications of AI in medicine are presented, such as pattern recognition in cancer diagnosis and therapy chatbots. The authors emphasize the importance of evaluating the impact of AI on responsibility and human authorship. Various actors are mentioned who should be involved in the development and application process of AI systems, and the importance of data protection standards and clear regulations for data usage is highlighted. It is emphasized that the competence of doctors and personal care remain of great importance and that ethical standards must be adhered to in order to ensure the well-being of patients. [Extracted from the article]

The article focuses on the role of information technology in medical laboratories, discussing the utilization of artificial intelligence and machine learning within the field. It highlights the prevalence of expert systems over true AI applications, emphasizes the importance of open platforms for laboratory software, and explores the potential of order entry systems for enhancing communication between laboratories and healthcare providers.

Background. Leadership, communities, and the individuals who produce technology all need to shift their value systems to use technology equitably and sustainably. Several examples of technology applications seek to undermine human rights, human justice, and fairness. As a result of the fact that it makes it simpler for patients to participate in the process of fulfilling their medical requirements, digital health has the ability to play a role in the innovation process that is taking place within the healthcare business. In today’s world, there has been considerable growth in the number of intelligent technologies and sophisticated equipment (such as smart wireless and wearable sensors) used to monitor and treat patients’ diseases rapidly. This is done by giving patients fast access to critical health indicators and conducting continual evaluations. Aims. The purpose of this research is to describe and examine novel methods for using newly developed technology in the medical industry. This study’s objective is to analyze the many ways modern technology is being incorporated into the healthcare system to serve patients better. We will primarily emphasize Artificial Intelligence (AI), Virtual Reality (VR), the use of Smart Phones, Exoskeletons, and several other types of robotic technology. Conclusions. Several uses of artificial intelligence (AI) technology may be found within the healthcare industry. These technologies have been developed to support medical imaging and diagnostic services, combat pandemics, provide virtual patient care, increase patient engagement and adherence to treatment plans, reduce the administrative burden on healthcare professionals, and monitor patients’ compliance with exercise regimens. Because of their ability to precisely assess patients’ development, cutting-edge technologies such as artificial intelligence (AI) should be included in augmented reality (AR) rehabilitation systems. In addition, creating a system based on augmented reality that can be used on mobile devices may help achieve other advantages, such as remote patient recovery monitoring and the provision of real-time attention at reduced cost. [ABSTRACT FROM AUTHOR]

Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis. [ABSTRACT FROM AUTHOR]

Background: Similar to understanding how blood pressure is measured by a sphygmomanometer, physicians will soon have to understand how an artificial intelligence–based application has come to the conclusion that a patient has hypertension, diabetes, or cancer. Although there are an increasing number of use cases where artificial intelligence is or can be applied to improve medical outcomes, the extent to which medical doctors and students are ready to work and leverage this paradigm is unclear. Objective: This research aims to capture medical students’ and doctors’ level of familiarity toward artificial intelligence in medicine as well as their challenges, barriers, and potential risks linked to the democratization of this new paradigm. Methods: A web-based questionnaire comprising five dimensions—demographics, concepts and definitions, training and education, implementation, and risks—was systematically designed from a literature search. It was completed by 207 participants in total, of which 105 (50.7%) medical doctors and 102 (49.3%) medical students trained in all continents, with most of them in Europe, the Middle East, Asia, and North America. Results: The results revealed no significant difference in the familiarity of artificial intelligence between medical doctors and students (P=.91), except that medical students perceived artificial intelligence in medicine to lead to higher risks for patients and the field of medicine in general (P<.001). We also identified a rather low level of familiarity with artificial intelligence (medical students=2.11/5; medical doctors=2.06/5) as well as a low attendance to education or training. Only 2.9% (3/105) of medical doctors attended a course on artificial intelligence within the previous year, compared with 9.8% (10/102) of medical students. The complexity of the field of medicine was considered one of the biggest challenges (medical doctors=3.5/5; medical students=3.8/5), whereas the reduction of physicians’skills was the most important risk (medical doctors=3.3; medical students=3.6; P=.03). Conclusions: The question is not whether artificial intelligence will be used in medicine, but when it will become a standard practice for optimizing health care. The low level of familiarity with artificial intelligence identified in this study calls for the implementation of specific education and training in medical schools and hospitals to ensure that medical professionals can leverage this new paradigm and improve health outcomes. [ABSTRACT FROM AUTHOR]

Dentistry is a technically oriented profession, and the health care sector is significantly influenced by the ubiquitous trend of digitalization. Some of these digital developments have the potential to result in disruptive changes for dental practice, while others may turn out to be just a pipedream. This Discovery! essay focuses on innovations built on artificial intelligence (AI) as the center-technology influencing 1) dental eHealth data management, 2) clinical and technical health care applications, and 3) services and operations. AI systems enable personalized dental medicine workflows by analyzing all eHealth data gathered from an individual patient. Besides dental-specific data, this also includes genomic, proteomic, and metabolomic information and therefore facilitates optimized and personalized treatment strategies and risk management. Based on the power of AI, the triangular frame of "data"/"health care"/"service" is supplemented by technological advancements in the field of social media, Internet of things, augmented and virtual reality, rapid prototyping, and intraoral optical scanning as well as teledentistry. Innovation continues to be critical to tackle dental problems until its routine implementation based on sound scientific evidence. Novel technologies must be viewed critically in relation to the cost-benefit ratio and the ethical implications of a misleading diagnosis or treatment produced by AI algorithms. Highly sensitive eHealth data must be handled responsibly to enable the immense benefits of these technologies to be realized for society. The focus on patient-centered research and the development of personalized dental medicine have the potential to improve individual and public health, as well as clarify the interconnectivity of disease in a more cost-effective way. [ABSTRACT FROM AUTHOR]

Will consumers accept artificial intelligence (AI) as a medical care provider? On the basis of evolution theory, we investigate the implicit psychological mechanisms that underlie consumers' interactions with medical AI and a human doctor. In a behavioral investigation (Study 1), consumers expressed a positive intention to use medical AI's healthcare services when it used personalized rather than mechanical conversation. However, neural investigation (Study 2) using functional magnetic resonance imaging revealed that some consumers' implicit attitudes toward medical AI differed from their expressed behavioral intentions. The brain areas linked with implicitly apathetic emotions were activated even when medical AI used a personalized conversation, whereas consumers' brains were activated in areas associated with prosociality when they interacted with a human doctor who used a personalized conversation. On the basis of our neural evidence, consumers perceive an identical personalized conversation differently when it is offered by a medical AI versus a human doctor. These findings have implications for the area of human–AI interactions and medical decision‐making and suggest that replacing human doctors with medical AI is still an unrealistic proposition. [ABSTRACT FROM AUTHOR]

Nadie discute los beneficios de la transformación digital en la industria de la salud. Los obstáculos tradicionales se desvanecen y la covid-19 ejerce un poderoso efecto catalizador. Quienes no asuman los cambios corren el riesgo de quedar fuera del mercado. [ABSTRACT FROM AUTHOR]

The article discusses the potential benefits and legal issues concerning the use of technology and artificial intelligence in the U.S. health care sector. Topics include the issues on the cost, quality, and access to care in the sector, the potential of artificial intelligence technology in enhancing quality of care and medical research, and the possible application of tort liability laws in regulating medical technology.

The article focuses on health care providers have begun to utilize technology, more specifically artificial intelligent technology (AI) and changing how patients are being treated by helping physicians improve patient care. It mentions use of AI may prove to further complicate the legal handling of medical errors and goal is consistent with the goal of medical negligence law. It also mentions personalized medicine is another way to use AI.

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms "diabetes" and "artificial intelligence." The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers. [ABSTRACT FROM AUTHOR]

The race to make the dream of artificial intelligence a reality comes parallel with the increasing struggle of health care systems to cope with information overload and translational pressure. It is clear that a shift in the way data is generated requires a shift in the way they are processed. This is where AI comes with great promises to solve the problem of volume versus applicability of information in science. In medicine, AI is showing exponential progress in the fields of predictive analysis and image recognition. These promises however, come with an intricate package of ethico-social, scientific and economic implications, towards which a reductionist approach leads to distorted and dramatic predictions. All this, in a time when the growing pressure on healthcare systems towards defensive medicine begs the question of the true need for AI for good medical practice. This article examines the concept and achievements of AI and attempts to offer a complex view on the realistic expectations from it in medicine, in the context of current practice (Ref. 38). [ABSTRACT FROM AUTHOR]

The article presents an editorial which discusses on an importance of need of artificial intelligence based computational and bioethics framework for healthcare based technology. The topics addressed include details on the Turing‑Test by Alan Turing where a human who converses with an unseen machine and an unseen human must guess which of the two is the machine and information on Watson supercomputer by International Business Machines Corp. (IBM).

The article focuses on use of machine learning for addressing microbiota data collected from molecular microbiota profiling tools like16S rRNA sequencing and metagenomics shotgun sequencing (MSS), in immunocompromised cancer patients. It mentions impact of microbiota composition on human health. It presents information on use of artificial intelligence (AI) combining computer science with statistics for development of automatic algorithms that identify complex relationships within data sets.

Background: Artificial intelligence (AI) is increasingly being used in patient care. In the future, physicians will need to understand not only the basic functioning of AI applications, but also their quality, utility, and risks. Methods: This article is based on a selective review of the literature on the principles, quality, limitations, and benefits AI applications in patient care, along with examples of individual applications. Results: The number of AI applications in patient care is rising, with more than 500 approvals in the United States to date. Their quality and utility are based on a number of interdependent factors, including the real-life setting, the type and amount of data collected, the choice of variables used by the application, the algorithms used, and the goal and implementation of each application. Bias (which may be hidden) and errors can arise at all these levels. Any evaluation of the quality and utility of an AI application must, therefore, be conducted according to the scientific principles of evidence-based medicine--a requirement that is often hampered by a lack of transparency. Conclusion: AI has the potential to improve patient care while meeting the challenge of dealing with an ever -increasing surfeit of information and data in medicine with limited human resources. The limitations and risks of AI applications require critical and responsible consideration. This can best be achieved through a combination of scientific [ABSTRACT FROM AUTHOR]

In Ayurveda system of medicine individuals are classified into seven constitution types, “Prakriti”, for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations. [ABSTRACT FROM AUTHOR]

Abstract: In clinical treatment processes, inpatient length of stay (LOS) is not only a readily available indicator of hospital activity, but also a reasonable proxy of resource consumption. Accurate inpatient LOS prediction has strong implications for health service delivery. Major techniques proposed (statistical approaches or artificial neuronal networks) consider a priori knowledge, such as demographics or patient physical factors, providing accurate methods to estimate LOS at early stages of the patient (admission). However, unexpected scenarios and variations are common places of clinical treatment processes that have a dramatical impact on the LOS. Therefore, these predictors should deal with adaptability, considering the temporal evolution of the patient. In this paper, we propose an inpatient LOS prediction approach across various stages of clinical treatment processes. This proposal relies on a kind of regularity assumption demanding that patient traces of the specific treatment process with similar medical behaviors have similar LOS. Therefore, this approach follows a Case-based Reasoning methodology since it predicts an inpatient LOS of a partial patient trace by referring to the past traces of clinical treatment processes that have similar medical behaviors with the current one. The proposal is evaluated using 284 patient traces from the pulmonary infection CTPs, extracted from Zhejiang Huzhou Central Hospital of China. [Copyright &y& Elsevier]

Abstract: In clinical treatment processes, inpatient length of stay (LOS) is not only a readily available indicator of hospital activity, but also a reasonable proxy of resource consumption. Accurate inpatient LOS prediction has strong implications for health service delivery. Major techniques proposed (statistical approaches or artificial neuronal networks) consider a priori knowledge, such as demographics or patient physical factors, providing accurate methods to estimate LOS at early stages of the patient (admission). However, unexpected scenarios and variations are commonplaces of clinical treatment processes that have a dramatical impact on the LOS. Therefore, these predictors should deal with adaptability, considering the temporal evolution of the patient. In this paper, we propose an inpatient LOS prediction approach across various stages of clinical treatment processes. This proposal relies on a kind of regularity assumption demanding that patient traces of the specific treatment process with similar medical behaviors have similar LOS. Therefore, this approach follows a Case-based Reasoning methodology since it predicts an inpatient LOS of a partial patient trace by referring to the past traces of clinical treatment processes that have similar medical behaviors with the current one. The proposal is evaluated using 284 patient traces from the pulmonary infection CTPs, extracted from Zhejiang Huzhou Central Hospital of China. [Copyright &y& Elsevier]

In this article, the author offers information on need of reframing the laws that can encourage innovative technology's growth to overcome the healthcare shortages and enhance the quality of patients care. It suggests that an approach of law for assessing the liability against artificial intelligence systems and robotics for advanced health care is required in the U.S.

Abstract: Objective: In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence – an AI that can “think like a doctor”. Methods: This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. Results: The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) – while at the same time the AI approach could obtain a 30–35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Conclusion: Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. [Copyright &y& Elsevier]

Background: Patient records contain valuable information regarding explanation of diagnosis, progression of disease, prescription and/or effectiveness of treatment, and more. Automatic recognition of clinically important concepts and the identification of relationships between those concepts in patient records are preliminary steps for many important applications in medical informatics, ranging from quality of care to hypothesis generation. Methods: In this work we describe an approach that facilitates the automatic recognition of eight relationships defined between medical problems, treatments and tests. Unlike the traditional bag-of-words representation, in this work, we represent a relationship with a scheme of five distinct context-blocks determined by the position of concepts in the text. As a preliminary step to relationship recognition, and in order to provide an end-to-end system, we also addressed the automatic extraction of medical problems, treatments and tests. Our approach combined the outcome of a statistical model for concept recognition and simple natural language processing features in a conditional random fields model. A set of 826 patient records from the 4th i2b2 challenge was used for training and evaluating the system. Results: Results show that our concept recognition system achieved an F-measure of 0.870 for exact span concept detection. Moreover the context-block representation of relationships was more successful (F-Measure = 0.775) at identifying relationships than bag-of-words (F-Measure = 0.402). Most importantly, the performance of the end-toend system of relationship extraction using automatically extracted concepts (F-Measure = 0.704) was comparable to that obtained using manually annotated concepts (F-Measure = 0.711), and their difference was not statistically significant. Conclusions: We extracted important clinical relationships from text in an automated manner, starting with concept recognition, and ending with relationship identification. The advantage of the context-blocks representation scheme was the correct management of word position information, which may be critical in identifying certain relationships. Our results may serve as benchmark for comparison to other systems developed on i2b2 challenge data. Finally, our system may serve as a preliminary step for other discovery tasks in medical informatics. [ABSTRACT FROM AUTHOR]

Background Supporting medical decision making is a complex task, that offers challenging research issues to Artificial Intelligence (AI) scientists. The Case-based Reasoning (CBR) methodology has been proposed as a possible means for supporting decision making in this domain since the 1980s. Nevertheless, despite the variety of efforts produced by the CBR research community, and the number of issues properly handled by means of this methodology, the success of CBR systems in medicine is somehow limited, and almost no research product has been fully tested and commercialized; one of the main reasons for this may be found in the nature of the problem domain, which is extremely complex and multi-faceted. Materials and methods In this environment, we propose to design a modular architecture, in which several AI methodologies cooperate, to provide decision support. In the resulting context CBR, originally conceived as a well suited reasoning paradigm for medical applications, can extend its original roles, and cover a set of additional tasks. Results and conclusions As an example, in the paper we will show how CBR can be exploited for configuring the parameters relied upon by other (reasoning) modules. Other possible ways of deploying CBR in this domain will be the object of our future investigations, and, in our opinion, a possible research direction for people working on CBR in the health sciences. [ABSTRACT FROM AUTHOR]

Abstract: In this study, different systems based on the fuzzy C-means (FCM) clustering algorithm are utilized for the detection of epileptic spikes in electroencephalogram (EEG) records. The systems are constructed as either single or two-stages. In contrast to single-stage systems, the two-stage system comprises a pre-classifier stage realized by a neural network. The FCM based two-stage system is also compared to a similar system implemented using the K-means clustering algorithm. The results imply that an FCM based two-stage system should be preferred as the spike detection system. [Copyright &y& Elsevier]

This Viewpoint summarizes the 2019 AI in Healthcare report from the National Academy of Medicine (NAM), which reviews best practices for AI development, adoption, and maintenance and urges prioritization of equity, inclusion, and human rights in AI health system implementation. [ABSTRACT FROM AUTHOR]

Summary: Objectives: This paper presents current work in case-based reasoning (CBR) in the health sciences, describes current trends and issues, and projects future directions for work in this field. Methods and material: It represents the contributions of researchers at two workshops on case-based reasoning in the health sciences. These workshops were held at the Fifth International Conference on Case-Based Reasoning (ICCBR-03) and the Seventh European Conference on Case-Based Reasoning (ECCBR-04). Results: Current research in CBR in the health sciences is marked by its richness. Highlighted trends include work in bioinformatics, support to the elderly and people with disabilities, formalization of CBR in biomedicine, and feature and case mining. Conclusion: CBR systems are being better designed to account for the complexity of biomedicine, to integrate into clinical settings and to communicate and interact with diverse systems and methods. [Copyright &y& Elsevier]