Supporting Interaction and Co-evolution of Users and Systems

Interactive systems supporting people activities, even those designed for a specific application domain, should be very flexible, i.e., they should be easily adaptable to specific needs of the user communities. They should even allow users to personalize the system to better fit with their evolving needs. This paper presents an original model of the interaction and coevolution processes occurring between humans and interactive systems and discusses an approach to design systems that supports such processes. The approach is based on the “artisan’s workshop” metaphor and foresees the participatory design of an interactive system as a network of workshops customized to different user communities and connected one another by communication paths. Such paths allow end users and members of the design team to trigger and actuate the co-evolution. The feasibility of the methodology is illustrated through a case study in the medical domain

An ontological view in telemedicine.

The verification and validation of information system models impact on the adequacy and appropriateness of using the value of telemedicine services for continuously optimizing healthcare outcomes. We have defined a methodology to help the modeling and rigorous analysis of the requirements of information systems in telemedicine. On one hand, this methodology will be based on a formal representation of requirements (systemic, generic domain, etc.) within a knowledge base that will be a requirements repository. On the other hand, this methodology will use conceptual graphs for the formalization of ontology of activities and the production of arguments related to the formal verification of models built from this ontology. We describe an example illustrating the engagement of conceptual graph procedures to model the contextual situations in the telemedicine development. We also discuss the way in which ethical issues will actually take place in telemedicine applications

Design and Mining of Health Information Systems for Process and Patient Care Improvement

abstract: In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and convenient access to diagnostic images from multiple modalities. How to integrate such HISs and best utilize their data remains a challenging problem due to the disparity of HISs as well as high-dimensionality and heterogeneity of the data. My PhD dissertation research includes three inter-connected and integrated topics and focuses on designing integrated HISs and further developing statistical models and machine learning algorithms for process and patient care improvement. Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients. Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks. Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Uses of AI in Field of Radiology- What is State of Doctor & Pateints Communication in Different Disease for Diagnosis Purpose

Over the course of the past ten years, there has been a rising interest in the application of AI in radiology with the goal of improving diagnostic practises. Every stage of the imaging workflow might potentially be improved by AI, beginning with the ordering of diagnostic procedures and ending with the distribution of data. One of the disadvantages of utilising AI in radiology is that it can disrupt the doctor-patient contact that takes place during the diagnostic procedure. This research synthesis examines how patients and clinicians engage with AI in the process of diagnosing cancer, brain disorders, gastrointestinal tract, and bone-related diseases. [S]ome of the diseases that are studied include cancer, brain disorders, and gastrointestinal tract.  Researchers began their investigation of several databases in 2021 and continued their work until 2023. Some of the databases that were examined include PubMed, Embase, Medline, Scopus, and PsycNet. The search terms "artificial intelligence" and "intelligence machine" as well as "communication," "radiology," and "oncology diagnosis" were utilised. It has been demonstrated that artificial intelligence can help medical professionals make more accurate diagnoses. Medical compliance can be enhanced with good training in doctor-patient diagnosis communication, and future research may assist boost patients\u27 trust by informing them of the benefits of AI. Both of these things are important for the delivery of quality medical care. GRAPHICAL ABSTRACT &nbsp

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.Comment: 23 pages, 22 figure

a comprehensive survey of medical doctor's perspectives in Portugal

Publisher Copyright: Copyright: © 2023 Pedro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.publishersversionpublishe