With the rapid surge in the prevalence of Large Language Models (LLMs), individuals are increasingly turning to conversational AI for initial insights across various domains, including health-related inquiries such as disease diagnosis. Many users seek potential causes on platforms like ChatGPT or Bard before consulting a medical professional for their ailment. These platforms offer valuable benefits by streamlining the diagnosis process, alleviating the significant workload of healthcare practitioners, and saving users both time and money by avoiding unnecessary doctor visits. However, Despite the convenience of such platforms, sharing personal medical data online poses risks, including the presence of malicious platforms or potential eavesdropping by attackers. To address privacy concerns, we propose a novel framework combining FHE and Deep Learning for a secure and private diagnosis system. Operating on a question-and-answer-based model akin to an interaction with a medical practitioner, this end-to-end secure system employs Fully Homomorphic Encryption (FHE) to handle encrypted input data. Given FHE's computational constraints, we adapt deep neural networks and activation functions to the encryted domain. Further, we also propose a faster algorithm to compute summation of ciphertext elements. Through rigorous experiments, we demonstrate the efficacy of our approach. The proposed framework achieves strict security and privacy with minimal loss in performance.

This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode medical image assisted diagnosis method based on the image is proposed. Through the test of various feature extraction methods, the optimal operating characteristic under curve product (AUROC) is 0.9985, the recall rate is 0.9814, and the accuracy is 0.9833. This method can be applied to clinical diagnosis, and it is a practical method. Any outpatient doctor can register quickly through the system, or log in to the platform to upload the image to obtain more accurate images. Through the system, each outpatient physician can quickly register or log in to the platform for image uploading, thus obtaining more accurate images. The segmentation of images can guide doctors in clinical departments. Then the image is analyzed to determine the location and nature of the tumor, so as to make targeted treatment.

Alzheimer's disease is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning-based approaches are popular and well-motivated models for many medical image processing tasks such as computer-aided diagnosis. These techniques can vastly improve the process for accurate diagnosis of Alzheimer's disease. In this paper, we investigate the performance of these techniques for Alzheimer's disease detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the powerful artificial neural network and support vector machines as classifiers, as well as principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches.

In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy supply, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play. Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above, and many more. It exploits and orchestrates i.a. techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, optimization, stochastics, statistics, decision making under uncertainty, machine learning, as well as calculus, combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.

In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.

ATLANTA -- An ultrasound-based artificial intelligence classifier of thyroid nodules identified benign nodules with sensitivity similar to fine-needle aspiration, according to data presented at ENDO 2022. "Artificial analysis of thyroid ultrasound images can identify nodules that are very unlikely to be malignant," Nikita Pozdeyev, MD, PhD, assistant professor at University of Colorado Anschutz Medical Campus, told Healio. "These are mostly spongiform nodules that have a less than 3% probability of malignancy." Pozdeyev and colleagues trained a supervised deep learning classifier of thyroid nodules on 32,545 images of 621 thyroid nodules acquired from University of Washington. The classifier was then tested on an independent set of 145 nodules collected from the University of Colorado.

Taipei, Dec. 9 (CNA) A diagnosis system that utilizes artificial intelligence (AI) for the rapid screening of dementia due to Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been developed by an interdisciplinary research team led by a professor at National Taiwan University of Science and Technology (NTUST). The electroencephalography (EEG)-based computer-aided diagnosis system, which integrates different technologies, including EEG signal processing, circuits and electronics, AI, cognitive neuroscience, and medical science, has demonstrated a high level of accuracy and usability in assisting AD and MCI diagnosis, according to the Ministry of Science and Technology (MOST) on Wednesday. As a result, the system is also expected to facilitate early intervention and reduce the risk of developing Alzheimer's dementia. Liu Yi-hung (劉益宏), a professor at NTUST who has led the research team since 2017, collaborated with Tsai Chia-fen (蔡佳芬), a doctor in the Division of Geriatric Psychiatry, Taipei Veterans General Hospital, and Wu Chien-te (吳建德), a professor at the International Research Center for Neurointelligence, University of Tokyo. They developed the nonlinearly multiple EEG feature decoding technique and identified the most MCI-sensitive brain areas by using machine learning methods.

Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections. Objective: To address these challenges, we designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data and to record and share diagnostic data in real-time with a centralized database. Design: We trained our system using multiple data sets, including US-based electronic medical records (EMRs) and open-source medical literature and developed an adaptive, general medical assistant system based on machine learning algorithms. Main outcomes and Measure: The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions. The application is unique from existing systems in that it covers a wide range of common diseases, signs, and medication typical in lower-resource countries; the application works with or without an active internet connection. Results: We have built and implemented an adaptive learning system that assists trained primary care professionals by means of an Android smartphone application, which interacts with a central database and collects real-time data. The application has been tested by dozens of primary care providers. Conclusions and Relevance: Our application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters. This application could be leveraged to automatically populate local or national EMR systems.

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.

Companies in Japan are proceeding to employ technology that helps provide diagnoses for pneumonia caused by COVID-19 by using artificial intelligence (AI) to analyze computed tomography (CT) images of patients' lungs. While the new technology is expected to lessen the burden on doctors, and its usage is a promising means to sustain the health care system, Chinese e-commerce giant Alibaba Group, which handles data inside and outside China including credit information and urban management, is paving the way in the development of a new AI diagnosis system. "We were able to apply the existing technology owned by Alibaba to combat the novel coronavirus in just two days," said Chi Ying, head of the medical AI team at Alibaba DAMO Academy, in a recent online interview. The academy, which is responsible for research on cutting-edge technology in the Alibaba Group, has been pushing research on AI analysis of CT scans since 2017. "Ground glass opacity," or a pattern of gray shadows, is a characteristic finding that indicates pneumonia caused by the novel coronavirus, and this was put to practical use to distinguish whether patients had pneumonia caused by the novel coronavirus, or pneumonia stemming from other causes.