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AI has developed from a basic set of "if, then rules" to more sophisticated algorithms that function similar to the human brain. Applications of AI in medicine have grown since the introduction of ML and DL, opening the door to individualised treatment rather than medicine just based on algorithms. The application of AI in medicine is in both diagnostics and surgery. This paper helps analyse the role of Artificial Intelligence in healthcare especially diagnostics and surgery. It also explores the role of AI in medical negligence. Part 1 talks about the application of ML and DL in healthcare, Part 2 analyses the test for medical negligence and Part 3 analyses the coexistence of AI and medical negligence. Part 4 examines the inclusion of innovation in AI in the tort of medical negligence. Lastly, Part 5 examines the ways in which liability can be assigned and provides for a theoretical framework. The paper concludes by providing a suggestion that the standard of care principles in the common law tort of negligence must offer a fault-based system which is sufficiently adaptable to allow for the application of developments in AI in healthcare and addresses the problems that arise as the technology advances. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has emerged as a transformative force in various sectors, offering both opportunities and challenges, particularly in a rapidly developing nation like India. This paper explores the dual aspects of AI as a boon and a bane, analysing its impact on economic growth, employment, healthcare, education, and ethical concerns. While AI promises enhanced productivity and innovation, it also poses risks such as job displacement, privacy violations, and algorithmic bias. This study aims to provide a balanced perspective on the implications of AI in India, highlighting the need for strategic frameworks that maximize benefits while mitigating risks. [ABSTRACT FROM AUTHOR]

The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems. [ABSTRACT FROM AUTHOR]

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards. [ABSTRACT FROM AUTHOR]

As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried's phenotype that is capable of detecting frailty. To determine user requirements, the system's architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) in medicine has attracted a great deal of attention in the medical literature, but less is known about how to assess the uncertainty of individual predictions in clinical applications. This paper demonstrates the use of Conformal Prediction (CP) to provide insight on racial stratification of uncertainty quantification for breast cancer risk prediction. The results presented here show that CP methods provide important information about the diminished quality of predictions for individuals of minority racial backgrounds. [ABSTRACT FROM AUTHOR]

Jordanian healthcare institutes have launched several programs since 2009 to establish health information systems (HISs). Nowadays, the generic expectation is that the use of HIS resources is performed on daily basis among healthcare staff. However, there can be still a noticeable barrier due to a lack of knowledge if medical doctors do not receive proper training on existing HISs. Moreover, the lack of studies on this area hinders the clarity about the received versus the required training skills among medical doctors. To support this research initiative, survey data have been collected from specialized medical doctors who are currently affiliated with five Jordanian universities to assess their need for HIS training. The results also aim to explore the extent of medical doctors' use of HIS resources in Jordan. Moreover, they examine whether medical doctors require additional training on using HIS resources or not, as well as the main areas of required training programs. Specifically, this paper highlights the main topics that can be suitable subjects for enhanced training programs. The results show that most respondents use HISs in their daily clinical practices. However, most of them have not taken professional training on such systems. Hence, most of the respondents reported the need for additional training programs on several aspects of HIS resources. Moreover, based on the survey results, the most significant areas that require training are biomedical data analysis, artificial intelligence in medicine, health care management, and recent advances in electronic health records, respectively. Therefore, specialized medical doctors in Jordan need training on extracting useful and potential features of HISs. Education and training professionals in healthcare are recommended to establish training programs in Jordanian healthcare centers, which can further improve the quality of healthcare. [ABSTRACT FROM AUTHOR]

Background: This paper looks at how AI and machine learning have been applied over the last ten years to the development of anti-cancer drugs. By speeding up the synthesis of more desirable compounds and the identification of new ones, artificial intelligence (AI) has demonstrated substantial contributions to the research and therapy of anti-cancer therapies. Methods: This work is a narrative review that examines numerous uses of AI-based techniques in the development of anti-cancer medications. Results: Future developments in human cancer research and treatment are anticipated to be significantly influenced by AI. Protein-interaction network analysis, drug target prediction, binding site prediction, and virtual screening are examples of innovative techniques. Drug design and screening are enhanced by machine learning, and the use of multitarget drug development approaches has made it possible to develop cancer treatments with fewer side effects. AI does, however, have several drawbacks, such as a heavy reliance on data and a narrow scope of explanation. Interpretable AI models, which combine data and computation in AI-assisted cancer treatment research, will be the new development path in the future. Conclusion: For more than thirty years, computer-aided drug design techniques have been a key component in the advancement of cancer therapies. Artificial intelligence is a new and powerful technology that has the potential to speed up, lower the cost, and improve the efficacy of anti-cancer therapy development. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Breast cancer seriously threatens women's physical and mental health. Mammography is one of the most effective methods for breast cancer diagnosis via artificial intelligence algorithms to identify diverse breast masses. The popular intelligent diagnosis methods require a large amount of breast images for training. However, collecting and labeling many breast images manually is extremely time consuming and inefficient. In this paper, we propose a distributed multi-latent code inversion enhanced Generative Adversarial Network (dm-GAN) for fast, accurate and automatic breast image generation. The proposed dm-GAN takes advantage of the generator and discriminator of the GAN framework to achieve automatic image generation. The new generator in dm-GAN adopts a multi-latent code inverse mapping method to simplify the data fitting process of GAN generation and improve the accuracy of image generation, while a multi-discriminator structure is used to enhance the discrimination accuracy. The experimental results show that the proposed dm-GAN can automatically generate breast images with higher accuracy, up to a higher 1.84 dB Peak Signal-to-Noise Ratio (PSNR) and lower 5.61% Fréchet Inception Distance (FID), as well as 1.38x faster generation than the state-of-the-art. [ABSTRACT FROM AUTHOR]

Artificial Intellegence is an assemblage of many algorithms for analysing and interpreting knowledge from vast collection of heterogenous data, which influenced a wide range of indusries. The concepts of AI are related to fields like statistics, probability, pattern recognition, machie learning etc. collectively called as “computational intelligence”. This paper analyses the impact of these techinques in the healthcare life cycle starting from diagnosis to treatment and also its contribution in prevention. As a small error in the applicability will lead to dangeous and ireversible effect, this paper analyses the means by which governments taking care of and ensuring their performance while giving permission for the products based on AI in healthcare. [ABSTRACT FROM AUTHOR]

Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy. [ABSTRACT FROM AUTHOR]

Significant developments are currently underway in the field of cancer research, particularly in Germany, regarding cancer registration and the use of medical information systems. The use of such systems contributes significantly to quality assurance and increased efficiency in data evaluation. The growing importance of artificial intelligence (AI) in cancer research is evident as these systems integrate AI for various purposes, i.e. to assist users in data analysis. This paper uses ensemble learning to classify the graphical user interface state of the medical information system CARESS. The results show that all ensemble learning models utilized achieved good performance. In particular, the gradient boosting algorithm performed the best with an accuracy of 97%. The results represent a starting point for further development of ensemble learning in medical data analysis, with the potential for integration into various applications such as recommender systems. [ABSTRACT FROM AUTHOR]

Generative Pretrained Transformer, often known as GPT, is an innovative kind of Artificial Intelligence (AI) which can produce writing that seems to have been written by a person. OpenAI created this AI language model called ChatGPT. It is built using the GPT architecture and is trained on a large corpus of text data to respond to natural language inquiries that resemble a person's requirements. This technology has lots of applications in healthcare. The need for accurate and current data is one of the major obstacles to adopting ChatGPT in healthcare. GPT must have access to precise and up-to-date medical data to provide trustworthy suggestions and treatment options. It might be accomplished by ensuring that the data used by GPT is received from reliable sources and that the data is updated regularly. Since sensitive medical information would be involved, it will also be crucial to consider privacy and security issues while utilising GPT in the healthcare industry. This paper briefs about ChatGPT and its need for healthcare, its significant Work Flow Dimensions and typical features of ChatGPT for the Healthcare domain. Finally, it identified and discussed significant applications of ChatGPT for healthcare. ChatGPT can comprehend the conversational context and provide contextually appropriate replies. Its effectiveness as a conversational AI tool makes it useful for chatbots, virtual assistants, and other applications. However, we see many limitations in medical ethics, data interpretation, accountability and other issues related to the privacy. Regarding specialised tasks like text creation, language translation, text categorisation, text summarisation, and creating conversation systems, ChatGPT has been pre-trained on a large corpus of text data, and somewhat satisfactory results can be expected. Moreover, it can also be utilised for various Natural Language Processing (NLP) activities, including sentiment analysis, part-of-speech tagging, and named entity identification. [ABSTRACT FROM AUTHOR]

Background: Coronavirus continues to spread worldwide, causing various health and economic disruptions. One of the most important approaches to controlling the spread of this disease is to use an artificial intelligence (AI)-based technological intervention, such as a chatbot system. Chatbots can aid in the fight against the spread of COVID-19. Objective: This paper introduces COVID-Bot, an intelligent interactive system that can help screen students and confirm their COVID-19 vaccination status. Methods: The design and development of COVID-Bot followed the principles of the design science research (DSR) process, which is a research method for creating a new scientific artifact. COVID-Bot was developed and implemented using the SnatchBot chatbot application programming interface (API) and its predefined tools, which are driven by various natural language processing algorithms. Results: An evaluation was carried out through a survey that involved 106 university students in determining the functionality, compatibility, reliability, and usability of COVID-Bot. The findings indicated that 92 (86.8%) of the participants agreed that the chatbot functions well, 85 (80.2%) agreed that it fits well with their mobile devices and their lifestyle, 86 (81.1%) agreed that it has the potential to produce accurate and consistent responses, and 85 (80.2%) agreed that it is easy to use. The average obtained α was .87, indicating satisfactory reliability. Conclusions: This study demonstrates that incorporating chatbot technology into the educational system can combat the spread of COVID-19 among university students. The intelligent system does this by interacting with students to determine their vaccination status. [ABSTRACT FROM AUTHOR]

The combination of "Big Data" and Artificial Intelligence (AI) is frequently promoted as having the potential to deliver valuable health benefits when applied to medical decision-making. However, the responsible adoption of AI-based clinical decision support systems faces several challenges at both the individual and societal level. One of the features that has given rise to particular concern is the issue of explainability, since, if the way an algorithm arrived at a particular output is not known (or knowable) to a physician, this may lead to multiple challenges, including an inability to evaluate the merits of the output. This "opacity" problem has led to questions about whether physicians are justified in relying on the algorithmic output, with some scholars insisting on the centrality of explainability, while others see no reason to require of AI that which is not required of physicians. We consider that there is merit in both views but find that greater nuance is necessary in order to elucidate the underlying function of explainability in clinical practice and, therefore, its relevance in the context of AI for clinical use. In this paper, we explore explainability by examining what it requires in clinical medicine and draw a distinction between the function of explainability for the current patient versus the future patient. This distinction has implications for what explainability requires in the short and long term. We highlight the role of transparency in explainability, and identify semantic transparency as fundamental to the issue of explainability itself. We argue that, in day-to-day clinical practice, accuracy is sufficient as an "epistemic warrant" for clinical decision-making, and that the most compelling reason for requiring explainability in the sense of scientific or causal explanation is the potential for improving future care by building a more robust model of the world. We identify the goal of clinical decision-making as being to deliver the best possible outcome as often as possible, and find--that accuracy is sufficient justification for intervention for today's patient, as long as efforts to uncover scientific explanations continue to improve healthcare for future patients. [ABSTRACT FROM AUTHOR]

This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of MLbased decision support systems and also enriches theories on human-AI augmentation. [ABSTRACT FROM AUTHOR]

Background: Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are self-reporting tools that can measure important information about patients, such as health priorities, experience, and perception of outcome. The use of traditional objective measures such as vital signs and lab values can be supplemented with these self-reported patient measures to provide a more complete picture of a patient's health status. Machine learning, the use of computer algorithms that improve automatically through experience, is a powerful tool in health care that often does not use subjective information shared by patients. However, machine learning has largely been based on objective measures and has been developed without patient or public input. Algorithms often do not have access to critical information from patients and may be missing priorities and measures that matter to patients. Combining objective measures with patient-reported measures can improve the ability of machine learning algorithms to assess patients' health status and improve the delivery of health care. Objective: The objective of this scoping review is to identify gaps and benefits in the way machine learning is integrated with patient-reported outcomes for the development of improved public and patient partnerships in research and health care. Methods: We reviewed the following 3 questions to learn from existing literature about the reported gaps and best methods for combining machine learning and patient-reported outcomes: (1) How are the public engaged as involved partners in the development of artificial intelligence in medicine (2) What examples of good practice can we identify for the integration of PROMs into machine learning algorithms (3) How has value-based health care influenced the development of artificial intelligence in health care We searched Ovid MEDLINE(R), Embase, PsycINFO, Science Citation Index, Cochrane Library, and Database of Abstracts of Reviews of Effects in addition to PROSPERO and the ClinicalTrials website. The authors will use Covidence to screen titles and abstracts and to conduct the review. We will include systematic reviews and overviews published in any language and may explore additional study types. Quantitative, qualitative, and mixed methods studies are included in the reviews. Results: The search is completed, and Covidence software will be used to work collaboratively. We will report the review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and Critical Appraisal Skills Programme for systematic reviews. Conclusions: Findings from our review will help us identify examples of good practice for how to involve the public in the development of machine learning systems as well as interventions and outcomes that have used PROMs and PREMs. [ABSTRACT FROM AUTHOR]

Despite its exponential growth, artificial intelligence (AI) in healthcare faces various challenges. One of them is a lack of explainability of AI medical devices, which arguably leads to insufficient trust in AI technologies, quality, and accountability and liability issues. The aim of this paper is to examine whether, why and to what extent AI explainability should be demanded with relation to AI-enabled medical devices and their outputs. Relying on a critical analysis of interdisciplinary literature on this topic and an empirical study, we conclude that the role of AI explainability in the medical AI context is a limited one. If narrowly defined, AI explainability principle is capable of addressing only a limited range of challenges associated with AI and is likely to reach fewer goals than sometimes expected. The study shows that, instead of technical explainability of medical AI devices, most stakeholders need more transparency around its development and quality assurance process. [ABSTRACT FROM AUTHOR]

Background: With the growing excitement of the potential benefits of using machine learning and artificial intelligence in medicine, the number of published clinical prediction models that use these approaches has increased. However, there is evidence (albeit limited) that suggests that the reporting of machine learning--specific aspects in these studies is poor. Further, there are no reviews assessing the reporting quality or broadly accepted reporting guidelines for these aspects. Objective: This paper presents the protocol for a systematic review that will assess the reporting quality of machine learning--specific aspects in studies that use machine learning to develop clinical prediction models. Methods: We will include studies that use a supervised machine learning algorithm to develop a prediction model for use in clinical practice (ie, for diagnosis or prognosis of a condition or identification of candidates for health care interventions). We will search MEDLINE for studies published in 2019, pseudorandomly sort the records, and screen until we obtain 100 studies that meet our inclusion criteria. We will assess reporting quality with a novel checklist developed in parallel with this review, which includes content derived from existing reporting guidelines, textbooks, and consultations with experts. The checklist will cover 4 key areas where the reporting of machine learning studies is unique: modelling steps (order and data used for each step), model performance (eg, reporting the performance of each model compared), statistical methods (eg, describing the tuning approach), and presentation of models (eg, specifying the predictors that contributed to the final model). Results: We completed data analysis in August 2021 and are writing the manuscript. We expect to submit the results to a peer-reviewed journal in early 2022. Conclusions: This review will contribute to more standardized and complete reporting in the field by identifying areas where reporting is poor and can be improved. [ABSTRACT FROM AUTHOR]

Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of an electronic stethoscope, (2) a decision-making subsystem, and (3) a subsystem for displaying and visualizing the results in an understandable form. The electronic stethoscope captures the patient's phonocardiographic sounds, filters and digitizes them, and then sends the resulting phonocardiographic sounds to the decision-making system. The decision-making systemclassifies sounds into normal and abnormal using machine learning techniques, and as a result identifies abnormal heart sounds. The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff, but also for patients and recommends further actions to patients. As a result of the study, we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%. More accurately, the proposed stethoscope can identify normal heart sounds with 93.5% accuracy, abnormal heart sounds with 93.25% accuracy. Moreover, speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination. [ABSTRACT FROM AUTHOR]

Blockchain, one of these new digital technologies, has special qualities like immutability, decentralization, and transparency that can be helpful in many different areas including managing electronic medical data and access rights, as well as mobile health. We reviewed all COVID-19-related and unrelated blockchain applications in the healthcare industry. MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar were searched for pertinent reports up to July 29, 2021. There were articles with both technical and clinical designs, with or without prototype development. A total of 85 375 articles were assessed, and 415 full-length reports -37 of which were connected to COVID-19 and 378 of which were unrelated--were ultimately incorporated into the study. The three primary COVID-19-related applications that were reported were contact tracing, monitoring of immunity or vaccination passports, and pandemic control and surveillance. Management of electronic medical records, internet of things (such as remote monitoring or mobile health), and supply chain monitoring were the top three non-COVID-19-related applications. The majority of publications (277 [667 %] of 415] focused on the technical performance of blockchain prototype systems, whereas nine (2 %) research indicated actual clinical use and uptake. Only technical studies (129 [311 %] of 415) made up the remaining investigations. The most popular platforms were Hyperledger and Ethereum. Numerous COVID-19-related and unrelated health care applications of blockchain technology are possible. The necessity to adapt fundamental blockchain technology for use in healthcare settings is highlighted by the fact that the majority of current research is still in the technical stage and only a small number offers practical clinical applications. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) systems hold great promise for the future development within a variety of sectors. At the same time, there is also great concern about harms and potential misuse of AI. Upscaling and implementing existing AI systems do already have the potential of affecting severely, and potentially irreversibly, fundamental social conditions for social interaction, professional autonomy, and political governance. Therefore, guiding principles and frameworks to support developers and governing authorities are emerging around the world to foster justified trust in AI research and innovation. Ultimately, these safeguarding institutions and mechanisms rely on human knowledge and wisdom. Health is an area that is expected to benefit from AI based technologies aimed at promoting beneficial, accurate and effective preventive and curative interventions. Also, machine learning technologies might be used to improve the accuracy of the evidence base for cost-effective and beneficial decisionmaking. How can bioethicists contribute to promote beneficial AI interventions and avoid harms produced by AI technology? What would be justified roles of bioethicists in development and use of AI systems? Method: The paper is based on literature review and philosophical reflection. Discussion: In this presentation, we will base our analysis on an analytical decomposition of the life cycle of AI systems into the phases of development, deployment and use. Furthermore, we will use a framework of translational ethics proposed by Bærøe, and identify a variety of structural tasks, as well as limitations to such, for bioethicists to undertake within this emerging multifold area of experts and disciplines. [ABSTRACT FROM AUTHOR]

ardiovascular imaging has become the foundation of heart failure diagnostic studies. The most crucial technique for clinically diagnosing cardiac diseases is echocardiography. Depending on the positioning and angles of the probe, different cardiac views can be obtained during echocardiography. Therefore, the automatic classification of echo views, especially for computer systems and even automatic diagnosis in later stages, is the first step for echocardiogram diagnosis. In addition, the classification of heart views allows the tagging of echo videos to be done on a high scale and the possibility of database management and collection is provided. However, deep learning is an advanced machine learning method that is used to analyze both natural and medical images. But so far, it has not been widely used on cardiac ultrasound, the reason is the complexity of formats with multiple views and multi-view formats of echocardiogram. The proposed topic of this research is to provide novel and effective architectures for cardiac view classification. The aim of this study is to overcome the challenges in processing, categorizing and recognizing echo views stored as videos and images. In particular, in order to extract features, automatic methods and deep networks have replaced manual methods. In the presented solution, by using the transfer learning and the 3d-cnn method in image and video classification, we have improved the accuracy of echo views classification. [ABSTRACT FROM AUTHOR]

Background: The use of Cloud-based storage of personal health records has increased globally. The GPOC Series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC Series' international survey on the necessity of a GPOC. Methods: Online global survey with invitations sent to health ministries and major organisations. It received answers from health ministries and affiliated advisors of all 193 United Nations (UN) member states, 2 UN observer states (Holy See & Palestine) and 1 de facto UN non-member state (Taiwan) and from 18 major international organisations. The survey examined a dozen aspects encompassing demographics, privacy, sharing, movability, co-ownership, research, company usage, regulation and the necessity of a GPOC. Results: The GPOC Survey elicited responses from 267 individuals from 214 entities, including all UN member states, and major international organisations. Twelve domains were identified, covering demographics, correctness, privacy, commercial use, medical and non-medical research, co-ownership, data sharing, record movement, ownership centralisation, patient rights, environmental concerns, and foundation creation. Results show high agreement on most issues, including support for co-ownership (89%) and movement of personal health records (84%). Disagreement was prominent regarding centralised ownership by the state (64%) and data sharing without consent (85%). Additionally, respondents expressed interest in a neutral, decentralised foundation for regulation (73%) and the environmental sustainability of electronic health records (84%). Conclusions: A Global Patient co-Owned Cloud (GPOC) of personal health records could significantly enhance patient independence and involvement in health management, supported by the near consensus agreement across various domains identified in our survey. This consensus underscores the potential of GPOC to democratise healthcare and align with UN Sustainable Development Goals (SDGs). The survey results demonstrate strong support for GPOC's role in promoting evidence-based patient management, reducing information silos, and fostering ethical data sharing. Moreover, the overwhelming agreement on key principles of co-ownership, data sharing, and environmental sustainability highlights the global inclination for a decentralised, patient-controlled PHR platform. This platform stands to empower patients worldwide, advance precision medicine, and contribute to the global development and dissemination of artificial intelligence in healthcare. [ABSTRACT FROM AUTHOR]

Background: The use of Cloud-based storage personal health records has increased globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Technical sandboxes allow the capability to simulate different scientific concepts before making them production ready. None exist for the medical fields and cloud-based research. Methods: We constructed and tested the sandbox using open-source infrastructures (Ubuntu, Alpine Linux, and Colaboratory) and demonstrated it on a cloud platform. Data preprocessing utilised standard and in-house libraries. The Mina protocol, implementing zero-knowledge proofs, ensured secure blockchain operations, while the Ethereum smart contract protocol within Hyperledger Besu supported enterprise-grade sandbox development. Results: Here, we present the GPOC series' technical sandbox. This is to facilitate future online research and testing of the concept and its security, encryption, movability, research potential, risks and structure. It has several protocols for homomorphic encryption, decentralisation, transfers, and file management. The sandbox is openly available online and tests authorisation, transmission, access control, and integrity live. It invites all committed parties to test and improve the platform. Individual patients, clinics, organisations and regulators are invited to test and develop the concept. The sandbox displays co-ownership of personal health records. Here it is trisected between patients, clinics and clinicians. Patients can actively participate in research and control their health data. The challenges include ensuring that a unified underlying protocol is maintained for cross-border delivery of care based on data management regulations. Conclusions: The GPOC concept, as demonstrated by the GPOC Sandbox, represents an advancement in healthcare technology. By promoting patient co-ownership and utilising advanced technologies like blockchain and homomorphic encryption, the GPOC initiative enhances individual control over health data and facilitates collaborative medical research globally. The justification for this research lies in its potential to improve evidence-based medicine and AI dissemination. The significance of the GPOC initiative extends to various aspects of healthcare, patient co-ownership of health data, promoting access to resources and healthcare democratisation. The implications include better global health outcomes through continued development and collaboration, ensuring the successful adoption of the GPOC Sandbox and advancing innovation in digital health. [ABSTRACT FROM AUTHOR]

Pure laparoscopic donor hepatectomy (PLDH) has become a standard practice for living donor liver transplantation in expert centers. Accurate understanding of biliary structures is crucial during PLDH to minimize the risk of complications. This study aims to develop a deep learning-based segmentation model for real-time identification of biliary structures, assisting surgeons in determining the optimal transection site during PLDH. A single-institution retrospective feasibility analysis was conducted on 30 intraoperative videos of PLDH. All videos were selected for their use of the indocyanine green near-infrared fluorescence technique to identify biliary structure. From the analysis, 10 representative frames were extracted from each video specifically during the bile duct division phase, resulting in 300 frames. These frames underwent pixel-wise annotation to identify biliary structures and the transection site. A segmentation task was then performed using a DeepLabV3+ algorithm, equipped with a ResNet50 encoder, focusing on the bile duct (BD) and anterior wall (AW) for transection. The model's performance was evaluated using the dice similarity coefficient (DSC). The model predicted biliary structures with a mean DSC of 0.728 ± 0.01 for BD and 0.429 ± 0.06 for AW. Inference was performed at a speed of 15.3 frames per second, demonstrating the feasibility of real-time recognition of anatomical structures during surgery. The deep learning-based semantic segmentation model exhibited promising performance in identifying biliary structures during PLDH. Future studies should focus on validating the clinical utility and generalizability of the model and comparing its efficacy with current gold standard practices to better evaluate its potential clinical applications. [ABSTRACT FROM AUTHOR]

Building on the reviewed literature, this paper intends to offer a synthesis of the main orientations that have been proposed for the future regulation of the Medical AI in Europe as well as its clinical, industrial and societal potential impacts. [ABSTRACT FROM AUTHOR]

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles. [ABSTRACT FROM AUTHOR]

Background: Due to the importance of evidence-based research in plastic surgery, the authors of this study aimed to assess the accuracy of ChatGPT in generating novel systematic review ideas within the field of craniofacial surgery. Methods: ChatGPT was prompted to generate 20 novel systematic review ideas for 10 different subcategories within the field of craniofacial surgery. For each topic, the chatbot was told to give 10 "general" and 10 "specific" ideas that were related to the concept. In order to determine the accuracy of ChatGPT, a literature review was conducted using PubMed, CINAHL, Embase, and Cochrane. Results: In total, 200 total systematic review research ideas were generated by ChatGPT. We found that the algorithm had an overall 57.5% accuracy at identifying novel systematic review ideas. ChatGPT was found to be 39% accurate for general topics and 76% accurate for specific topics. Conclusion: Craniofacial surgeons should use ChatGPT as a tool. We found that ChatGPT provided more precise answers with specific research questions than with general questions and helped narrow down the search scope, leading to a more relevant and accurate response. Beyond research purposes, ChatGPT can augment patient consultations, improve healthcare equity, and assist in clinical decisionmaking. With rapid advancements in artificial intelligence (AI), it is important for plastic surgeons to consider using AI in their clinical practice to improve patient-centered outcomes. [ABSTRACT FROM AUTHOR]

Nearly 1% of people worldwide suffer from epilepsy. Electroencephalogram (EEG)-based diagnostics and monitoring tools, such as scalp EEG, subscalp EEG, stereo EEG, or sub/epi-dural EEG recordings [also known as electrocorticography (ECoG)], are widely used in different settings as the gold standard techniques to perform seizure identification, localization, and more primarily in epilepsy or suspected epilepsy in patients. Techniques such as subscalp EEG and ECoG offer long-term brain interaction, potentially replacing traditional electroceuticals with smart closed-loop therapies. However, these systems require continuous on-device training due to real-time demands and high power consumption. Inspired by the brain architecture, biologically plausible algorithms, such as some neuromorphic computing, show promise in addressing these challenges. In our research, we utilized liquid time-constant spiking neural networks with forward propagation through time to detect seizures in scalp-EEG. We trained and validated our model on the Temple University Hospital dataset and tested its generalization on out-of-sample data from the Royal Prince Alfred Hospital (RPAH) and EPILEPSIAE datasets. Our model achieved high area under the receiver operating characteristic curve (AUROC) scores of 0.83 in both datasets. We assessed the robustness by decreasing the memory size by 90% and obtained an overall AUROC of 0.82 in the RPAH dataset and 0.83 in the EPILEPSIAE dataset. Our model showed outstanding results of 3.1 μJ power consumption per inference and a 20% firing rate during training. This allows for incorporating bio-inspired efficient algorithms for on-device training, tackling challenges such as memory, power consumption, and efficiency. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has an increasing role to play in medical education and has great potential to revolutionize health professional education systems overall. However, this is accompanied by substantial questions concerning technical and ethical risks which are of particular importance because the quality of medical education has a direct effect on physical and psychological health and wellbeing. This article establishes an overarching distinction of AI across two typological dimensions, functional and humanistic. As indispensable foundations, these are then related to medical practice overall, and forms of implementation with examples are described in both general and medical education. Increasingly, the conditions for successful medical education will depend on an understanding of AI and the ethical issues surrounding its implementation, as well as the formulation of appropriate guidelines by regulatory and other authorities. Within that discussion, the limits of both narrow or Routine AI (RAI) and artificial general intelligence or Decision AI (DAI) are examined particularly in view of the ethical need for Trustworthy AI (TAI) as part of the humanistic dimension. All stakeholders, from patients to medical practitioners, managers, and institutions, need to be able to trust AI, and loss of confidence could be catastrophic in some cases. [ABSTRACT FROM AUTHOR]

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The human race is forced to engage in a very rapid adaptation process whenever it is confronted with technological change in any sphere of life. The unabated progress of artificial intelligence (AI) has also impacted the field of critical thinking. It is fascinating that critical thinking, an essential component of intellectual intelligence in nursing, seems to be disrupted by an artificial brainalike machine that can automatically analyze and synthesize a series of contexts. This has been an improvisation of ideas of intellectual intelligence for a very long time. Perspectives on both sides of the coin bring up interesting questions about the role that AI will play in the future, such as whether it will disrupt the critical thinking skills of nurses or whether it may be engaged as a tool to increase the critical thinking skills capability, especially in the fields of nursing care. [ABSTRACT FROM AUTHOR]

Background: The growing significance of social media in commercial enterprises is bringing this theme to the attention of decision-makers. These days, businesses use Facebook, Twitter, and YouTube as part of their marketing strategies. This encourages communication between consumers and marketers. Similar communication tactics are used in the pharmaceutical sector. However, because this is a healthcare-related industry, there are a lot of rules that apply to it, especially to its marketing department. Purpose: The purpose of this study is to assess the pharmaceutical industry's online presence on social media sites like Facebook, Twitter, and YouTube, as well as to describe the various digital engagement tactics that are employed. Conclusion: The study's conclusions indicate that not all pharmaceutical businesses use social media, and that certain platforms are more popular than others. It's interesting to note that different social media platforms underwent different digital engagement techniques, and that the level of involvement was unrelated to the size of the companies. This study offers insights into the social media organization of pharmaceutical businesses and ostensibly supplies a framework and technique for further research in this area. Furthermore, a few of the constraints found offer guidance for future research directions. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.

The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like tuberculosis. Tuberculosis, a global public health challenge, demands swift interventions to prevent its spread. While deep learning and image processing techniques show potential in extracting meaningful insights from complex radiological images, their accuracy is often scrutinized due to a lack of explainability. This research navigates the intersection of AI and tuberculosis diagnosis by focusing on explainable artificial intelligence (XAI). A meticulously designed deep learning model for tuberculosis detection is introduced alongside an exploration of XAI to unravel complex decisions. The core belief is that XAI, by elucidating diagnostic decision rationale, enhances the reliability of AI in clinical settings. Emphasizing the pivotal role of XAI in tuberculosis diagnosis, this study aims to impact future research and practical implementations, fostering the adoption of AI-driven disease diagnosis methodologies for global health improvement. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is defined as the simulation of human intelligence by a digital computer or robotic system and has become a hype in current conversations. A subcategory of AI is deep learning, which is based on complex artificial neural networks that mimic the principles of human synaptic plasticity and layered brain architectures, and uses large-scale data processing. AI-based image analysis in breast screening programmes has shown noninferior sensitivity, reduces workload by up to 70% by pre-selecting normal cases, and reduces recall by 25% compared to human double reading. Natural language programs such as ChatGPT (OpenAI) achieve 80% and higher accuracy in advising and decision making compared to the gold standard: human judgement. This does not yet meet the necessary requirements for medical products in terms of patient safety. The main advantage of AI is that it can perform routine but complex tasks much faster and with fewer errors than humans. The main concerns in healthcare are the stability of AI systems, cybersecurity, liability and transparency. More widespread use of AI could affect human jobs in healthcare and increase technological dependency. AI in senology is just beginning to evolve towards better forms with improved properties. Responsible training of AI systems with meaningful raw data and scientific studies to analyse their performance in the real world are necessary to keep AI on track. To mitigate significant risks, it will be necessary to balance active promotion and development of quality-assured AI systems with careful regulation. AI regulation has only recently included in transnational legal frameworks, as the European Union's AI Act was the first comprehensive legal framework to be published, in December 2023. Unacceptable AI systems will be banned if they are deemed to pose a clear threat to people's fundamental rights. Using AI and combining it with human wisdom, empathy and affection will be the method of choice for further, fruitful development of tomorrow's senology. [ABSTRACT FROM AUTHOR]

Biomedical text document classification is an essential task within Natural Language Processing (NLP), with applications ranging from sentiment analysis to authorship identification. Despite advancements in traditional machine-learning algorithms like Support Vector Machines (SVM) and Logistic Regression, challenges such as data sparsity and high dimensionality persist. Recent years have seen a surge in the use of deep learning models to mitigate these issues. This study aims to conduct a comparative analysis of various machine-learning algorithms for classifying biomedical text documents. The study employs the "Medical Text Dataset - Cancer Doc Classification" from Kaggle, comprising 7570 biomedical text documents labeled into three types of cancer (colon, lung, and thyroid). A preprocessing pipeline involving tokenization, stop-word removal, and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization is applied. Algorithms including Logistic Regression, SVM, and Multinomial Naive Bayes are evaluated through 5-fold cross-validation. Performance metrics like accuracy, precision, recall, F1 score, and area under the ROC curve (AUC ROC) are employed. Logistic Regression outperforms the other algorithms with an accuracy of 78.3% and an AUC ROC of 88.59%. SVM and Multinomial Naive Bayes follow with lower performance metrics. Hyperparameter tuning further enhances the performance of the algorithms, particularly Logistic Regression. The study makes a significant contribution to the field of biomedical text classification by systematically comparing machine-learning algorithms. Logistic Regression emerges as the most effective, emphasizing the importance of algorithm selection and hyperparameter tuning in machine learning applications within this domain. [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]