Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy.

BACKGROUND AND AIMS: Patients' perception of their bowel cleansing quality may guide rescue cleansing strategies before colonoscopy. The main aim of this study was to train and validate a convolutional neural network (CNN) for classifying rectal effluent during bowel preparation intake as "adequate" or "inadequate" cleansing before colonoscopy. PATIENTS AND METHODS: Patients referred for outpatient colonoscopy were asked to provide images of their rectal effluent during the bowel preparation process. The images were categorized as adequate or inadequate cleansing based on a predefined 4-picture quality scale. A total of 1203 images were collected from 660 patients. The initial dataset (799 images), was split into a training set (80%) and a validation set (20%). The second dataset (404 images) was used to develop a second test of the CNN accuracy. Afterward, CNN prediction was prospectively compared with the Boston Bowel Preparation Scale (BBPS) in 200 additional patients who provided a picture of their last rectal effluent. RESULTS: On the initial dataset, a global accuracy of 97.49%, a sensitivity of 98.17% and a specificity of 96.66% were obtained using the CNN model. On the second dataset, an accuracy of 95%, a sensitivity of 99.60% and a specificity of 87.41% were obtained. The results from the CNN model were significantly associated with those from the BBPS (P<0.001), and 77.78% of the patients with poor bowel preparation were correctly classified. CONCLUSION: The designed CNN is capable of classifying "adequate cleansing" and "inadequate cleansing" images with high accuracy.

Artificial intelligence for dysplasia detection during surveillance colonoscopy in patients with ulcerative colitis: A cross-sectional, non-inferiority, diagnostic test comparison study.

BACKGROUND AND STUDY AIM: High-definition virtual chromoendoscopy, along with targeted biopsies, is recommended for dysplasia surveillance in ulcerative colitis patients at risk for colorectal cancer. Computer-aided detection (CADe) systems aim to improve colonic adenoma detection, however their efficacy in detecting polyps and adenomas in this context remains unclear. This study evaluates the CADe Discovery™ system's effectiveness in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer. PATIENTS AND METHODS: A prospective cross-sectional, non-inferiority, diagnostic test comparison study was conducted on ulcerative colitis patients undergoing colorectal cancer surveillance colonoscopy between January 2021 and April 2021. Patients underwent virtual chromoendoscopy (VCE) with iSCAN 1 and 3 with optical enhancement. One endoscopist, blinded to CADe Discovery™ system results, examined colon sections, while a second endoscopist concurrently reviewed CADe images. Suspicious areas detected by both techniques underwent resection. Proportions of dysplastic lesions and patients with dysplasia detected by VCE or CADe were calculated. RESULTS: Fifty-two patients were included, and 48 lesions analyzed. VCE and CADe each detected 9 cases of dysplasia (21.4% and 20.0%, respectively; p=0.629) in 8 patients and 7 patients (15.4% vs. 13.5%, respectively; p=0.713). Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy for dysplasia detection using VCE or CADe were 90% and 90%, 13% and 5%, 21% and 2%, 83% and 67%, and 29.2% and 22.9%, respectively. CONCLUSIONS: The CADe Discovery™ system shows similar diagnostic performance to VCE with iSCAN in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer.

Artificial intelligence-assisted identification and retrieval of chronic hepatitis C patients lost to follow-up in the health area of Pontevedra and O Salnés (Spain).

OBJECTIVE: Direct-acting antivirals (DAAs) to treat hepatitis C virus (HCV) infection offer an opportunity to eliminate the disease. This study aimed to identify and relink to care HCV patients previously lost to medical follow-up in the health area of Pontevedra and O Salnés (Spain) using an artificial intelligence-assisted system. PATIENTS AND METHODS: Active retrospective search of previously diagnosed HCV cases recorded in the Galician Health Service proprietary health information exchange database using the Herramientas para la EXplotación de la INformación (HEXIN) application. RESULTS AND CONCLUSIONS: Out of 99 lost patients identified, 64 (64.6%) were retrieved. Of these, 62 (96.88%) initiated DAA treatment and 54 patients (87.1%) achieved a sustained virological response. Mean time from HCV diagnosis was over 10 years. Main reasons for loss to follow-up were fear of possible adverse effects of treatment (30%) and mobility impediments (21%). Among the retrieved patients, almost one in three presented advanced liver fibrosis (F3) or cirrhosis (F4) at evaluation. In sum, HCV patients lost to follow-up can be retrieved by screening past laboratory records. This strategy promotes the achievement of HCV elimination goals.

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities.

Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.

[Mobile health in primary care. New challenges in the development of solutions to promote physical activity and well-being]. / La salud móvil en atención primaria. Nuevos desafíos en el desarrollo de soluciones para promover la actividad física y el bienestar.

The use of smart devices such as mobile phones (smartphones) or smart watches (smartwatch) to promote physical activity and well-being has increased in recent years among patients and professionals in primary care. This change is driven by the access of patients and professionals to a large catalog of health applications, which can complement the provision of services and promote the empowerment of patients in their own health and lifestyles. These applications are beginning to be integrated with areas such as Artificial Intelligence (AI), the Internet of Medical Things (IoMT) and data storage in the cloud, among other emerging technological systems, offering a new complementary approach to clinical practice known so far. Despite the great potential, there are numerous limitations and major challenges for its full implementation in clinical practice.

[Analysis of the challenges and dilemmas that bioethics of the 21st century will face in the digital health era]. / Análisis de retos y dilemas que deberá afrontar la bioética del siglo xxi, en la era de la salud digital.

The medical history underscores the significance of ethics in each advancement, with bioethics playing a pivotal role in addressing emerging ethical challenges in digital health (DH). This article examines the ethical dilemmas of innovations in DH, focusing on the healthcare system, professionals, and patients. Artificial Intelligence (AI) raises concerns such as confidentiality and algorithmic biases. Mobile applications (Apps) empower but pose challenges of access and digital literacy. Telemedicine (TM) democratizes and reduces healthcare costs but requires addressing the digital divide and interconsultation dilemmas; it necessitates high-quality standards with patient information protection and attention to equity in access. Wearables and the Internet of Things (IoT) transform healthcare but face ethical challenges like privacy and equity. 21st-century bioethics must be adaptable as DH tools demand constant review and consensus, necessitating health science faculties' preparedness for the forthcoming changes.

[Translated article] Bioethical Conflicts in Current Dermatology: A Narrative Review.

Both the functions and equipment of dermatologists have increased over the past few years, some examples being cosmetic dermatology, artificial intelligence, tele-dermatology, and social media, which added to the pharmaceutical industry and cosmetic selling has become a source of bioethical conflicts. The objective of this narrative review is to identify the bioethical conflicts of everyday dermatology practice and highlight the proposed solutions. Therefore, we conducted searches across PubMed, Web of Science and Scopus databases. Also, the main Spanish and American deontological codes of physicians and dermatologists have been revised. The authors recommend declaring all conflicts of interest while respecting the patients' autonomy, confidentiality, and privacy. Cosmetic dermatology, cosmetic selling, artificial intelligence, tele-dermatology, and social media are feasible as long as the same standards of conventional dermatology are applied. Nonetheless, the deontological codes associated with these innovations need to be refurbished.

Bioethical Conflicts in Current Dermatology: A Narrative Review. / Conflictos bioéticos en la dermatología actual: una revisión narrativa.

Both the functions and equipment of dermatologists have increased over the past few years, some examples being cosmetic dermatology, artificial intelligence, tele-dermatology, and social media, which added to the pharmaceutical industry and cosmetic selling has become a source of bioethical conflicts. The objective of this narrative review is to identify the bioethical conflicts of everyday dermatology practice and highlight the proposed solutions. Therefore, we conducted searches across PubMed, Web of Science and Scopus databases. Also, the main Spanish and American deontological codes of physicians and dermatologists have been revised. The authors recommend declaring all conflicts of interest while respecting the patients' autonomy, confidentiality, and privacy. Cosmetic dermatology, cosmetic selling, artificial intelligence, tele-dermatology, and social media are feasible as long as the same standards of conventional dermatology are applied. Nonetheless, the deontological codes associated with these innovations need to be refurbished.

The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going.

Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.

ChatGPT: opportunities and risks in the fields of medical care, teaching, and research.

ChatGPT is a virtual assistant with artificial intelligence (AI) that uses natural language to communicate, i.e., it holds conversations as those that would take place with another human being. It can be applied at all educational levels, including medical education, where it can impact medical training, research, the writing of scientific articles, clinical care, and personalized medicine. It can modify interactions between physicians and patients and thus improve the standards of healthcare quality and safety, for example, by suggesting preventive measures in a patient that sometimes are not considered by the physician for multiple reasons. ChatGPT potential uses in medical education, as a tool to support the writing of scientific articles, as a medical care assistant for patients and doctors for a more personalized medical approach, are some of the applications discussed in this article. Ethical aspects, originality, inappropriate or incorrect content, incorrect citations, cybersecurity, hallucinations, and plagiarism are some examples of situations to be considered when using AI-based tools in medicine.

ChatGPT es un asistente virtual con inteligencia artificial que utiliza lenguaje natural para comunicarse, es decir, mantiene conversaciones como las que se tendrían con otro humano. Puede aplicarse en educación a todos los niveles, que incluye la educación médica, en donde puede impactar en la formación, la investigación, la escritura de artículos científicos, la atención clínica y la medicina personalizada. Puede modificar la interacción entre médicos y pacientes para mejorar los estándares de calidad de la atención médica y la seguridad, por ejemplo, al sugerir medidas preventivas en un paciente que en ocasiones no son consideradas por el médico por múltiples causas. Los usos potenciales del ChatGPT en la educación médica, como una herramienta de ayuda en la redacción de artículos científicos, un asistente en la atención para pacientes y médicos para una práctica más personalizada, son algunas de las aplicaciones que se analizan en este artículo. Los aspectos éticos, originalidad, contenido inapropiado o incorrecto, citas incorrectas, ciberseguridad, alucinaciones y plagio son ejemplos de las situaciones a tomar en cuenta al usar las herramientas basadas en inteligencia artificial en medicina.

Practical foundations of machine learning for addiction research. Part I. Methods and techniques.

Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field underuses machine learning. This two-part narrative review focuses on machine learning tools and concepts, providing an introductory insight into their capabilities to facilitate their understanding and acquisition by addiction researchers. This first part presents supervised and unsupervised methods such as linear models, naive Bayes, support vector machines, artificial neural networks, and k-means. We illustrate each technique with examples of its use in current addiction research. We also present some open-source programming tools and methodological good practices that facilitate using these techniques. Throughout this work, we emphasize a continuum between applied statistics and machine learning, we show their commonalities, and provide sources for further reading to deepen the understanding of these methods. This two-part review is a primer for the next generation of addiction researchers incorporating machine learning in their projects. Researchers will find a bridge between applied statistics and machine learning, ways to expand their analytical toolkit, recommendations to incorporate well-established good practices in addiction data analysis (e.g., stating the rationale for using newer analytical tools, calculating sample size, improving reproducibility), and the vocabulary to enhance collaboration between researchers who do not conduct data analyses and those who do.

Practical foundations of machine learning for addiction research. Part II. Workflow and use cases.

In a continuum with applied statistics, machine learning offers a wide variety of tools to explore, analyze, and understand addiction data. These tools include algorithms that can leverage useful information from data to build models; these models can solve particular tasks to answer addiction scientific questions. In this second part of a two-part review on machine learning, we explain how to apply machine learning methods to addiction research. Like other analytical tools, machine learning methods require a careful implementation to carry out a reproducible and transparent research process with reliable results. This review describes a workflow to guide the application of machine learning in addiction research, detailing study design, data collection, data pre-processing, modeling, and results communication. How to train, validate, and test a model, detect and characterize overfitting, and determine an adequate sample size are some of the key issues when applying machine learning. We also illustrate the process and particular nuances with examples of how researchers in addiction have applied machine learning techniques with different goals, study designs, or data sources as well as explain the main limitations of machine learning approaches and how to best address them. A good use of machine learning enriches the addiction research toolkit.

Artificial Intelligence in Dermatology: A Threat or an Opportunity? / Inteligencia artificial en dermatología: ¿amenaza u oportunidad?

The worldwide explosion of interest in artificial intelligence (AI) has created a before-and-after moment in our lives by generating great improvements in such sectors as the automotive and food production industries. AI has even been called the fourth industrial revolution. Machine learning through AI is helping to improve professional processes and promises to transform the health care sector as we know it in various ways: 1) through applications able to promote health in the general population by providing high-quality information and offering advice for different segments of the population based on prediction models; 2) by developing prediction models based on anonymized clinical data, for preventive purposes in primary care; 3) by analyzing images to provide additional decision-making support for health care providers, for improving specialist care at the secondary level; and 4) through robotics applied to processes that promote health and well-being. However, the medical profession harbors doubts about whether this revolution is a threat or an opportunity owing to a lack of understanding of AI technology and the methods used to validate its applications. This article outlines basic aspects of AI as it is applied in dermatology and reviews the main advances achieved in the last 5 years.

[Artificial intelligence and its applications in medicine II: Current importance and practical applications]. / La inteligencia artificial y sus aplicaciones en medicina II: importancia actual y aplicaciones prácticas.

Technology and medicine follow a parallel path during the last decades. Technological advances are changing the concept of health and health needs are influencing the development of technology. Artificial intelligence (AI) is made up of a series of sufficiently trained logical algorithms from which machines are capable of making decisions for specific cases based on general rules. This technology has applications in the diagnosis and follow-up of patients with an individualized prognostic evaluation of them. Furthermore, if we combine this technology with robotics, we can create intelligent machines that make more efficient diagnostic proposals in their work. Therefore, AI is going to be a technology present in our daily work through machines or computer programs, which in a more or less transparent way for the user, will become a daily reality in health processes. Health professionals have to know this technology, its advantages and disadvantages, because it will be an integral part of our work. In these two articles we intend to give a basic vision of this technology adapted to doctors with a review of its history and evolution, its real applications at the present time and a vision of a future in which AI and Big Data will shape the personalized medicine that will characterize the 21st century.

Reflections around ethics, human intelligence and artificial intelligence.

On the internet, artificial intelligence has grown to become a program with codes and algorithms that learn and reprogram themselves to carry out pre-established tasks with greater efficiency; although this translates into improvements, the scope of the results and reprogramming are unknown to the programmer. Given the risk of deviation from pre-established objectives and ethical regulations, filters must be installed at the beginning, during and at the end of the process, as alarms for detecting deviations with bioethical implications. The interaction of human intelligence with artificial intelligence has had negative and positive disagreements. Initially, adapting regulations, labor laws and human rights was enough; now it is necessary for ethical standards to be established, such as those formulated in the Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe.

En internet ha crecido la inteligencia artificial hasta convertirse en un programa con códigos y algoritmos que aprenden y se reprograman para efectuar tareas preestablecidas con mayor eficiencia; si bien lo anterior se traduce en mejoría, el programador desconoce los alcances de los resultados y de la reprogramación. Ante el riesgo de desviación de los objetivos preestablecidos y de los reglamentos éticos, se tienen que implementar filtros al inicio, durante y al final del proceso, como alarmas cuando existan desviaciones con implicación bioética. La interacción de la inteligencia humana con la inteligencia artificial ha tenido desencuentros negativos y positivos. Al principio, bastó con adecuar normas, leyes laborales y derechos humanos; ahora se requiere establecer normas éticas, como las formuladas en la Declaración de Barcelona para el Adecuado Desarrollo y Uso de la Inteligencia Artificial en Europa.

Fundamental insights on when social network data are most critical for conservation planning.

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state-dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small-scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species-poor sites are subsets of species-rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small-scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un-nested and when social networks are likely to be centralized.

Ideas Fundamentales sobre Cuándo Son Más Importantes los Datos de las Redes Sociales para la Planeación de la Conservación Resumen Conforme se aceleran las declinaciones de la biodiversidad, existe una exigencia urgente para asegurar que cada dólar que se gasta en conservación contribuya a la protección de las especies. La planeación sistemática de la conservación es una estrategia usada extensivamente para lograr esto, aunque cada vez existe una mayor preocupación por que integre las dimensiones sociales humanas de la conservación para que sea una estrategia efectiva. Aun así, es insuficiente el conocimiento fundamental sobre cuándo son más importantes los datos sociales para orientar a las decisiones de planeación de la conservación. Para tratar con este problema identificamos los principios novedosos que sirven como guía para la inversión estratégica en la información de las redes sociales para la planeación sistemática de la conservación. Consideramos un problema común para la conservación; identificar con cuáles actores sociales, dentro de una red social, interactuar para incentivar el comportamiento de conservación que maximice el número de especies protegidas. Usamos simuladores de redes sociales y de especies distribuidas a lo largo de nodos de redes para identificar las estrategias dependientes del estado más convenientes y el valor de la información provenientes de las redes sociales. Hicimos lo anterior para una gama de estructuras de redes de motivos y distribución de especies y aplicamos la estrategia a una pesquería a pequeña escala en Kenia. El valor de la información proveniente de las redes sociales depende firmemente tanto de la distribución de las especies como de la estructura de la red social. Cuando las distribuciones de las especies se encontraban extremadamente anidadas (es decir, cuando los sitios pobres en cuanto a cantidad de especies son subconjuntos de sitios ricos en cantidad de especies), el valor de la información proveniente de las redes sociales casi siempre fue bajo. Esto sugiere que la información sobre cómo se distribuyen las especies en una comunidad es crítica para determinar si invertir o no en la recolección de datos provenientes de las redes sociales. Como contraste, el valor de este tipo de información fue mucho mayor cuando las redes sociales estaban sumamente centralizadas. Los resultados de la pesquería a pequeña escala fueron compatibles con las simulaciones. Nuestros resultados sugieren que la recolección estratégica de datos a partir de las redes sociales debería ser prioridad cuando las distribuciones de las especies no se encuentran anidadas y cuando sea probable que las redes sociales estén centralizadas.

Review of computational methods for the detection and classification of polyps in colonoscopy imaging. / Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia.

Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.

[Artificial intelligence and its applications in medicine I: introductory background to AI and robotics]. / La inteligencia artificial y sus aplicaciones en medicina I: introducción antecedentes a la IA y robótica.

Technology and medicine follow a parallel path during the last decades. Technological advances are changing the concept of health and health needs are influencing the development of technology. Artificial intelligence (AI) is made up of a series of sufficiently trained logical algorithms from which machines are capable of making decisions for specific cases based on general rules. This technology has applications in the diagnosis and follow-up of patients with an individualized prognostic evaluation of them. Furthermore, if we combine this technology with robotics, we can create intelligent machines that make more efficient diagnostic proposals in their work. Therefore, AI is going to be a technology present in our daily work through machines or computer programs, which in a more or less transparent way for the user, will become a daily reality in health processes. Health professionals have to know this technology, its advantages and disadvantages, because it will be an integral part of our work. In these two articles we intend to give a basic vision of this technology adapted to doctors with a review of its history and evolution, its real applications at the present time and a vision of a future in which AI and Big Data will shape the personalized medicine that will characterize the 21st century.

Ethics and artificial intelligence.

The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence (AI) involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental.

Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology.

The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.