Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Retrieval-augmented generation (RAG) is a well-suited technique for retrieving privacy-sensitive Electronic Health Records (EHR). It can serve as a key module of the healthcare copilot, helping reduce misdiagnosis for healthcare practitioners and patients. However, the diagnostic accuracy and specificity of existing heuristic-based RAG models used in the medical domain are inadequate, particular...
Deep learning models for medical image classification tasks are becoming widely implemented in AI-assisted diagnostic tools, aiming to enhance diagnostic accuracy, reduce clinician workloads, and improve patient outcomes. However, their vulnerability to adversarial attacks poses significant risks to patient safety. Current attack methodologies use general techniques such as model querying or pix...
Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-...
LLM agents will likely communicate on behalf of users with other entity-representing agents on tasks involving long-horizon plans with interdependen...
As digital technologies advance, communication networks face challenges in handling the vast data generated by intelligent devices. Autonomous vehic...
Recent advances in our ability to collect and process information, particularly through artificial intelligence, opens up some exciting possibilities ...
Compositionality and compositional generalization--the ability to understand novel combinations of known concepts--are central characteristics of hu...
Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in ...
Mammographic screening is an effective method for detecting breast cancer, facilitating early diagnosis. However, the current need to manually inspe...
State-of-the-art methods for backpropagation-free learning employ local error feedback to direct iterative optimisation via gradient descent. In thi...
Intelligent vehicular communication with vehicle road collaboration capability is a key technology enabled by 6G, and the integration of various vis...
As the use of Generative AI (GenAI) tools becomes more prevalent in interpersonal communication, understanding their impact on social perceptions is...
The communication scenarios and channel characteristics of 6G will be more complex and difficult to characterize. Conventional methods for channel p...
Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL...
Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we...
Recent large language models (LLMs) have demonstrated significant advancements, particularly in their ability to serve as agents thereby surpassing ...
As communication systems transition from symbol transmission to conveying meaningful information, sixth-generation (6G) networks emphasize semantic ...
Objective Structured Clinical Examinations (OSCEs) are widely used to assess medical students' communication skills, but scoring interview-based ass...
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a global model by exchanging on...
In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered ...