AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments

• URL: http://arxiv.org/abs/2211.14313v1
• Date: Mon, 21 Nov 2022 06:59:01 GMT
• Title: AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments
• Authors: Tim Tianyi Yang, Tom Tianze Yang, Andrew Liu, Jie Tang, Na An, Shaoshan Liu, Xue Liu
• Abstract summary: We introduce AICOM-MP, an AI-based monkeypox detector specially aiming for handling images taken from resource-constrained devices. Compared to existing AI-based monkeypox detectors, AICOM-MP has achieved state-of-the-art (SOTA) performance. We have also open sourced both the source code and the dataset of AICOM-MP to allow health AI professionals to integrate AICOM-MP into their services.
• Score: 14.025980747648571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Under the Autonomous Mobile Clinics (AMCs) initiative, we are developing, open sourcing, and standardizing health AI technologies to enable healthcare access in least developed countries (LDCs). We deem AMCs as the next generation of health care delivery platforms, whereas health AI engines are applications on these platforms, similar to how various applications expand the usage scenarios of smart phones. Facing the recent global monkeypox outbreak, in this article, we introduce AICOM-MP, an AI-based monkeypox detector specially aiming for handling images taken from resource-constrained devices. Compared to existing AI-based monkeypox detectors, AICOM-MP has achieved state-of-the-art (SOTA) performance. We have hosted AICOM-MP as a web service to allow universal access to monkeypox screening technology. We have also open sourced both the source code and the dataset of AICOM-MP to allow health AI professionals to integrate AICOM-MP into their services. Also, through the AICOM-MP project, we have generalized a methodology of developing health AI technologies for AMCs to allow universal access even in resource-constrained environments.

Related papers

• HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions [76.42274173122328]
  We present HAICOSYSTEM, a framework examining AI agent safety within diverse and complex social interactions. We run 1840 simulations based on 92 scenarios across seven domains (e.g., healthcare, finance, education) Our experiments show that state-of-the-art LLMs, both proprietary and open-sourced, exhibit safety risks in over 50% cases.
  arXiv  Detail & Related papers  (2024-09-24T19:47:21Z)
• Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey [82.84057882105931]
  Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) We present the fundamentals of GAI, MoE, and their interplay applications in IoV. We discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation.
  arXiv  Detail & Related papers  (2024-04-25T06:22:21Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative [2.501673623074516]
  EMPAIA is an open and vendor-neutral initiative to integrate artificial intelligence in pathology. We developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface.
  arXiv  Detail & Related papers  (2023-12-22T11:15:16Z)
• Integration and Implementation Strategies for AI Algorithm Deployment with Smart Routing Rules and Workflow Management [0.37918614538294315]
  This paper reviews the challenges hindering the widespread adoption of artificial intelligence (AI) solutions in the healthcare industry. The absence of standardized frameworks for AI development pose significant barriers and require a new paradigm to address them. The role of interoperability is examined in this paper as a crucial factor in connecting disparate applications within healthcare.
  arXiv  Detail & Related papers  (2023-11-17T19:38:37Z)
• FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
  Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
  arXiv  Detail & Related papers  (2023-08-11T10:49:05Z)
• A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC [54.74071593520785]
  Mobile AIGC can be a key enabling technology for an emerging application, called human digital twin (HDT) HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services.
  arXiv  Detail & Related papers  (2023-07-22T15:59:03Z)
• AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach [0.9928779798117072]
  Artificial Intelligence Clinics on Mobile (AICOM) is an open-source project devoted to answering the United Nations Sustainable Development Goal 3 on health. The core motivation for the AICOM project is the fact that over 80% of the people in the least developed countries (LDCs) own a mobile phone. We plan to continue expanding and open-sourcing the AICOM platform, aiming for it to evolve into an universal AI doctor for the Underserved and Hard-to-Reach.
  arXiv  Detail & Related papers  (2023-06-17T11:59:03Z)
• MONAI: An open-source framework for deep learning in healthcare [24.465436846127762]
  This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MonAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework.
  arXiv  Detail & Related papers  (2022-11-04T18:35:00Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Pervasive Communications Technologies For Managing Pandemics [3.908842679355255]
  This paper is about using smart technologies such as Mobile Edge Clouds (MEC), Internet of Things (IoT), and Artificial Intelligence (AI) as an approach to effectively manage pandemics. MECs provide cloud services on the edge, integrating IoT infrastructure and execution of sophisticated AI algorithms in the Cloud. Low-cost Single Board Computers (SBC) based clusters are integrated within MEC to support remote medical teams in the field.
  arXiv  Detail & Related papers  (2020-06-18T18:46:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.