Danny Xie

Healthcare systems are currently adapting to digital technologies, producing large quantities of novel data. Based on these data, machine-learning algorithms have been developed to support practitioners in labor-intensive workflows such as diagnosis, prognosis, triage or treatment of disease. However, their translation into medical practice is often hampered by a lack of careful evaluation in different settings. Efforts have started worldwide to establish guidelines for evaluating machine learning for health (ML4H) tools, highlighting the necessity to evaluate models for bias, interpretability, robustness, and possible failure modes. However, testing and adopting these guidelines in practice remains an open challenge. In this work, we target the paper-to-practice gap by applying an ML4H audit framework proposed by the ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) to three use cases: diagnostic prediction of diabetic retinopathy, diagnostic prediction of Alzheimer’s disease, and cytomorphologic classification for leukemia diagnostics. The assessment comprises dimensions such as bias, interpretability, and robustness. Our results highlight the importance of fine-grained and caseadapted quality assessment, provide support for incorporating proposed quality assessment considerations of ML4H during the entire development life cycle, and suggest improvements for future ML4H reference evaluation frameworks.

This article describes an interactive methodology to teach Artificial Intelligence (AI) through the contructivism philosophy of learning by doing, using, open source electronic platforms, like Arduino, Snap Circuits, Raspberry Pi and Circuit Playground, with an interactive hands-on approach Workshops. These are provided to high school and non-engineering students by (previously trained) engineering students volunteers. The methodology proposed is designed to highlight, in different learning activities, key concepts about Artificial Intelligence (AI). AI abstractes the human intelligence processes through algorithms and computer systems, taking advantage of the amount of data generated nowadays to create innovative, effective, efficient, accurate and at low cost solutions, applied in different fields. The main purpose is to motivate the participants to explode its creativity, improving their innovation skills to provide solutions for XXI century problems, better quality of life, health, among others. A survey will be conducted for the students to find insights about effectiveness of the proposed methodology to better acquire knowledge about AI knowledge. We encourage instructors to use similar interactive hands-on methodologies and to include AI concepts with STEM activities into general education courses. Other concerns of AI, is about the fairness of these algorithms, the inclusion and diversity is a key player in how these systems are built, and it can have consequences as the person perspective when building it The idea of the need for diversity and inclusion of the AI field.

This research introduces a simple framework for Object Detection (OD) based on few-shot methods and Visual Foundation Models (VFM). The framework comprises of three core modules: (i) object proposal, (ii) embedding creation, and (iii) object classification. We evaluated six distinct VFMs to generate the object proposals. We compared the performances of four feature extractors to optimize the object representation, including convolutional neural networks and transformer-based models. Furthermore, we investigated four few-shot methods for classifying objects using minimal labeled data. Our framework provides a scalable and cost-effective solution, specifically applied to OD for pineapple localization in the drone imagery of large pineapple fields, where labeled data are scarce and expensive.

We introduce PineSORT, a novel Multiple Object Tracking (MOT) system for drone-based agricultural monitoring, specifically tracking pineapples for yield estimation. Our approach tackles key challenges such as repetitive patterns, similar object appearances, low frame rates, and drone motion effects. PineSORT enhances tracking accuracy with motion direction cost, camera motion compensation, a three-stage association strategy, and overlap management. To handle large displacements, we propose an ORB-based camera compensation technique that significantly improves Association Accuracy (AssA). Evaluated via 5-fold cross-validation against BoTSORT and AgriSORT, PineSORT achieves statistically significant gains in Identity-Switch Penalized IDF1 (ISP-IDF1), IDF1, HOTA, and AssA metrics, confirming its effectiveness for tracking low-FPS drone footage and its value for precision agriculture.