Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, highlighting the potential for deep learning algorithms to improve diagnostic workflow in dermatopathology of highly routine diagnoses. Additionally, convolutional neural networks can support the diagnosis of melanoma and may help predict disease outcomes. Capabilities of machine learning in dermatopathology can extend beyond clinical diagnosis to education and research. Intelligent tutoring systems can teach visual diagnoses in inflammatory dermatoses, with measurable cognitive effects on learners. Natural language interfaces can instruct dermatopathology trainees to produce diagnostic reports that capture relevant detail for diagnosis in compliance with guidelines. Furthermore, deep learning can power computation- and population-based research. However, there are many limitations of deep learning that need to be addressed before broad incorporation into clinical practice. The current potential of AI in dermatopathology is to supplement diagnosis, and dermatopathologist guidance is essential for the development of useful deep learning algorithms. Herein, the recent progress of AI in dermatopathology is reviewed with emphasis on how deep learning can influence diagnosis, education, and research.

Disseminating research findings using a massive online open course for maximising impact and developing recommendations for practice.

BACKGROUND: Developing recommendations for how we deliver healthcare is often left to leading experts in a field. Findings from the Integrated Palliative Care in cancer and chronic conditions (InSup-C) study, which aimed to identify best practice in integrated palliative care in cancer, chronic obstructive pulmonary disease (COPD) and heart failure, led to recommendations developed through an expert consultation process. We also wanted to develop these recommendations further with participants who were largely clinicians and members of the public. METHODS: Results from the InSup-C study were disseminated through a three-week massive open online course (MOOC) which ran in 2016, 2017 and 2019. The first course helped develop the final recommendations, which were ranked by MOOC participants in the subsequent courses. MOOC participants were predominantly clinicians, but also academics and members of the public. They rated how important each recommendation was on a 9 point scale (9 most important). Descriptive statistics were used to analyse the ratings. The results were compared to findings from the consultation. RESULTS: Five hundred fifteen completed the last part of the course where the recommendations were ranked, of which 195 (38%) completed the ratings. The top recommendations related to: need to expand palliative care to non-malignant conditions; palliative care needs to include different dimensions of care including physical, psychological and spiritual; policies and regulations assessments should be made regularly; palliative care integration should be mandatory; and there should be greater availability of medicines. These differed compared to the top ranked recommendations by the consultation panel in relation to the importance of leadership and policy making. This may indicate that clinicians are more focused on daily care rather than the (inter) national agenda. CONCLUSIONS: Whilst both sets of recommendations are important, our study shows that we need to include the views of clinicians and the public rather than rely upon leading expert opinion alone. To keep recommendations fresh we need both the input of clinicians, the public and experts. When disseminating findings, MOOCs offer a useful way to gain greater reach with clinicians and the public, and importantly could be a vehicle to validate recommendations made by leading expert panels.

Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research.

The wide adoption of electronic medical records and subsequent availability of large amounts of clinical data provide a rich resource for researchers. However, the secondary use of clinical data for research purposes is not without limitations. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review to identify current issues related to secondary use of electronic medical record data via MEDLINE and CINAHL databases. All articles published until June 2018 were included. Sixty articles remained after title and abstract review, and four domains of potential limitations were identified: (1) data quality issues, present in 91.7% of the articles reviewed; (2) data preprocessing challenges (53.3%); (3) privacy concerns (18.3%); and (4) potential for limited generalizability (21.7%). Researchers must be aware of the limitations inherent to the use of electronic medical record data for research and consider the potential effects of these limitations throughout the entire study process, from initial conceptualization to the identification of adequate sources that can provide data appropriate for answering the research questions, analysis, and reporting study results. Consideration should also be given to using existing data quality assessment frameworks to facilitate use of standardized data quality definitions and further efforts of standard data quality reporting in publications.

Big data analytics in health: an overview and bibliometric study of research activity.

OBJECTIVE: The study presents an overview of the research activity in Big Data Analytics (BDA) in the field of health and demonstrates the existing knowledge through related examples. The objective is to inform health librarians about the nature and magnitude of the technological innovations in health information analysis tools, its influence, and where and how further material could be searched. METHODS: We performed a bibliometric and co-citation analysis within a total of 804 papers published between 2000 and 2016 and retrieved from the Web of Science and Scopus databases. Using the NVivo text analysis software, we identified the stakeholders of BDA in health and innovative decision support systems in the field. RESULTS: Our findings show a tremendous increase in published papers after 2014. Most of them are relevant to neurology and medical oncology. The stakeholders are clinicians, researchers, patients, administrators, IT specialists, vendors and policymakers. New BDA tools in medicine are mostly developed for disease monitoring purposes while they utilise visualisation to identify disease patterns and statistical analysis of past data for making predictions. CONCLUSIONS: Health analytics provide a unique opportunity for advancing health information research and medical decision making. It provides health information professionals with new tools in problem-solving offering new perspectives in prognosis and diagnosis of diseases.

RPPA: Origins, Transition to a Validated Clinical Research Tool, and Next Generations of the Technology.

RPPA technology has graduated from a research tool to an essential component of clinical drug discovery research and personalized medicine. Next generations of RPPA technology will be a single clinical instrument that integrates all the steps of the workflow.