DERMA: A melanoma diagnosis platform based on collaborative multilabel analog reasoning

The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multilabel case-based reasoning subsystems called DERMA. The system has to face up several challenges that include data characterization, pattern matching, reliable diagnosis, and self-explanation capabilities. Experiments using subsystems specialized in confocal and dermoscopy images have provided promising results for helping experts to assess melanoma diagnosis

Ajuda al Diagnòstic de Càncer de Melanoma amb Raonament Analògic Multietiqueta

La mortalitat provocada pel càncer de melanoma ha augmentat en els últims anys a causa, principalment, dels nous hàbits d'exposició al sol. Atenent al criteri mèdic, el diagnòstic precoç s'ha convertit en el millor mètode de prevenció. No és però una tasca trivial ja que els experts del domini han de fer front a un problema caracteritzat per tenir un gran volum de dades, de format heterogeni i amb coneixement parcial. A partir d'aquestes necessitats es proposa la creació d'una eina de suport a la presa de decisions que sigui capaç d'ajudar els experts en melanoma en el seu diagnòstic. El sistema ha de fer front a diversos reptes plantejats, que inclouen la caracterització del domini, la identificació de patrons a les dades segons el criteri dels experts, la classificació de nous pacients i la capacitat d'explicar els pronòstics obtinguts. Aquestes fites s'han materialitzat en la plataforma DERMA, la qual està basada en la col•laboració de diversos subsistemes de raonament analògic multietiqueta. L'experimentació realitzada amb el sistema proposat utilitzant dades d'imatges confocals i dermatoscòpiques ha permès comprovar la fiabilitat del sistema. Els resultats obtinguts han estat validats pels experts en el diagnòstic del melanoma considerant-los positius.La mortalidad a causa del cáncer de melanoma ha aumentado en los últimos años debido, principalmente, a los nuevos hábitos de exposición al sol. Atendiendo al criterio médico, el diagnóstico precoz se ha convertido en el mejor método de prevención, pero no se trata de una tarea trivial puesto que los expertos del dominio deben hacer frente a un problema caracterizado por tener un gran volumen de datos, de formato heterogéneo y con conocimiento parcial. A partir de estas necesidades se propone la creación de una herramienta de ayuda a la toma de decisiones que sea capaz de ayudar a los expertos en melanoma en su diagnóstico. El sistema tiene que hacer frente a diversos retos planteados, que incluyen la caracterización del dominio, la identificación de patrones en los datos según el criterio médico, la clasificación de nuevos pacientes y la capacidad de explicar los pronósticos obtenidos. Estas metas se han materializado en la plataforma DERMA la cual está basada en la colaboración de varios subsistemas de razonamiento analógico multietiqueta. La experimentación realizada con el sistema propuesto utilizando datos de imágenes confocales y dermatoscópicas ha permitido verificar la fiabilidad del sistema. Los resultados obtenidos han sido validados por los expertos en el diagnóstico del melanoma considerándolos positivos.Mortality related to melanoma cancer has increased in recent years, mainly due to new habits of sun exposure. Considering the medical criteria, early diagnosis has become the best method of prevention but this is not trivial because experts are facing a problem characterized by a large volume of data, heterogeneous, and with partial knowledge. Based on these requirements we propose the creation of a decision support system that is able to assist experts in melanoma diagnosis. The system has to cope with various challenges, that include the characterization of the domain, the identification of data patterns attending to medical criteria, the classification of new patients, and the ability to explain predictions. These goals have been materialized in DERMA platform that is based on the collaboration of several analogical reasoning multi-label subsystems. The experiments conducted with the proposed system using confocal and dermoscopic images data have been allowed to ascertain the reliability of the system. The results have been validated by experts in diagnosis of melanoma considering it as positive

DERMA: A Melanoma Diagnosis Platform Based on Collaborative Multilabel Analog Reasoning

The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multilabel case-based reasoning subsystems called DERMA. The system has to face up several challenges that include data characterization, pattern matching, reliable diagnosis, and self-explanation capabilities. Experiments using subsystems specialized in confocal and dermoscopy images have provided promising results for helping experts to assess melanoma diagnosis

Interpreting and Correcting Medical Image Classification with PIP-Net

Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net's decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net's unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging

Applications of interpretability in deep learning models for ophthalmology

PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT FINDINGS: The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' 'interpretability' and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice

The Spectrum of Clinical Utilities in Molecular Pathology Testing Procedures for Inherited Conditions and Cancer: A Report of the Association for Molecular Pathology

Clinical utility describes the benefits of each laboratory test for that patient. Many stakeholders have adopted narrow definitions for the clinical utility of molecular testing as applied to targeted pharmacotherapy in oncology, regardless of the population tested or the purpose of the testing. This definition does not address all of the important applications of molecular diagnostic testing. Definitions consistent with a patient-centered approach emphasize and recognize that a clinical test result\u27s utility depends on the context in which it is used and are particularly relevant to molecular diagnostic testing because of the nature of the information they provide. Debates surrounding levels and types of evidence needed to properly evaluate the clinical value of molecular diagnostics are increasingly important because the growing body of knowledge, stemming from the increase of genomic medicine, provides many new opportunities for molecular testing to improve health care. We address the challenges in defining the clinical utility of molecular diagnostics for inherited diseases or cancer and provide assessment recommendations. Starting with a modified analytic validity, clinical validity, clinical utility, and ethical, legal, and social implications model for addressing clinical utility of molecular diagnostics with a variety of testing purposes, we recommend promotion of patient-centered definitions of clinical utility that appropriately recognize the valuable contribution of molecular diagnostic testing to improve patient care

Towards Generalist Biomedical AI

Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To enable the development of these models, we first curate MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system. Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. Med-PaLM M reaches performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. We also report examples of zero-shot generalization to novel medical concepts and tasks, positive transfer learning across tasks, and emergent zero-shot medical reasoning. To further probe the capabilities and limitations of Med-PaLM M, we conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales. In a side-by-side ranking on 246 retrospective chest X-rays, clinicians express a pairwise preference for Med-PaLM M reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility. While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems

Social analytics for health integration, intelligence, and monitoring

Nowadays, patient-generated social health data are abundant and Healthcare is changing from the authoritative provider-centric model to collaborative and patient-oriented care. The aim of this dissertation is to provide a Social Health Analytics framework to utilize social data to solve the interdisciplinary research challenges of Big Data Science and Health Informatics. Specific research issues and objectives are described below. The first objective is semantic integration of heterogeneous health data sources, which can vary from structured to unstructured and include patient-generated social data as well as authoritative data. An information seeker has to spend time selecting information from many websites and integrating it into a coherent mental model. An integrated health data model is designed to allow accommodating data features from different sources. The model utilizes semantic linked data for lightweight integration and allows a set of analytics and inferences over data sources. A prototype analytical and reasoning tool called “Social InfoButtons” that can be linked from existing EHR systems is developed to allow doctors to understand and take into consideration the behaviors, patterns or trends of patients’ healthcare practices during a patient’s care. The tool can also shed insights for public health officials to make better-informed policy decisions. The second objective is near-real time monitoring of disease outbreaks using social media. The research for epidemics detection based on search query terms entered by millions of users is limited by the fact that query terms are not easily accessible by non-affiliated researchers. Publically available Twitter data is exploited to develop the Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS provides four visual analytics tools for monitoring epidemics, i.e., Instance Map, Distribution Map, Filter Map, and Sentiment Trend to investigate public health threats in space and time. The third objective is to capture, analyze and quantify public health concerns through sentiment classifications on Twitter data. For traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues, due to their expenses and significant time delays. A two-step sentiment classification model is built to measure the concern. In the first step, Personal tweets are distinguished from Non-Personal tweets. In the second step, Personal Negative tweets are further separated from Personal Non-Negative tweets. In the proposed classification, training data is labeled by an emotion-oriented, clue-based method, and three Machine Learning models are trained and tested. Measure of Concern (MOC) is computed based on the number of Personal Negative sentiment tweets. A timeline trend of the MOC is also generated to monitor public concern levels, which is important for health emergency resource allocations and policy making. The fourth objective is predicting medical condition incidence and progression trajectories by using patients’ self-reported data on PatientsLikeMe. Some medical conditions are correlated with each other to a measureable degree (“comorbidities”). A prediction model is provided to predict the comorbidities and rank future conditions by their likelihood and to predict the possible progression trajectories given an observed medical condition. The novel models for trajectory prediction of medical conditions are validated to cover the comorbidities reported in the medical literature