AI-empowered drug recommendation has become an important task in healthcare research areas, which offers an additional perspective to assist human doctors with more accurate and more efficient drug prescriptions. Generally, drug recommendation is based on patients' diagnosis results in the electronic health records. We assume that there are three key factors to be addressed in drug recommendation: 1) elimination of recommendation bias due to limitations of observable information, 2) better utilization of historical health condition and 3) coordination of multiple drugs to control safety. To this end, we propose DrugRec, a causal inference based drug recommendation model. The causal graphical model can identify and deconfound the recommendation bias with front-door adjustment. Meanwhile, we model the multi-visit in the causal graph to characterize a patient's historical health conditions. Finally, we model the drug-drug interactions (DDIs) as the propositional satisfiability (SAT) problem, and solving the SAT problem can help better coordinate the recommendation. Comprehensive experiment results show that our proposed model achieves state-of-the-art performance on the widely used datasets MIMIC-III and MIMIC-IV, demonstrating the effectiveness and safety of our method.

In this paper, we address this gap by dividing a typical patient's clinical journey

Smith was about to get brain surgery, but Musk's virtual appearance foretold a greater transformation. Smith's brain was about to be inducted into a much larger technology and media ecosystem--one of whose goals, the billionaire has said, is to achieve a "symbiosis" of humans and AI. Consider what unfolded on April 27, the day Smith announced on X that he'd received the brain implant and wanted to take questions. One of the first came from "Adrian Dittmann," an account often suspected of being Musk's alter ego. Can you describe how it feels to type and interact with technology overall using the Neuralink?" It feels wild, like I'm a cyborg from a sci-fi movie, moving a cursor just by thinking about it. At first, it was a struggle--my cursor acted like a drunk mouse, barely hitting targets, but after weeks of training with imagined hand and jaw movements, it clicked, almost like riding a bike."

Liver diseases pose a significant global health burden, impacting many individuals and having substantial economic and social consequences. Rising liver problems are considered a fatal disease in many countries, such as Egypt and Moldova. This study aims to develop a diagnosis and treatment model for liver disease using Basic Formal Ontology (BFO), Patient Clinical Data (PCD) ontology, and detection rules derived from a decision tree algorithm. For the development of the ontology, the National Viral Hepatitis Control Program (NVHCP) guidelines were used, which made the ontology more accurate and reliable. The Apache Jena framework uses batch processing to detect events based on these rules. Based on the event detected, queries can be directly processed using SPARQL. We convert these Decision Tree (DT) and medical guidelines-based rules into Semantic Web Rule Language (SWRL) to operationalize the ontology. Using this SWRL in the ontology to predict different types of liver disease with the help of the Pellet and Drools inference engines in Protege Tools, a total of 615 records were taken from different liver diseases. After inferring the rules, the result can be generated for the patient according to the rules, and other patient-related details, along with different precautionary suggestions, can be obtained based on these results. These rules can make suggestions more accurate with the help of Explainable Artificial Intelligence (XAI) with open API-based suggestions. When the patient has prescribed a medical test, the model accommodates this result using optical character recognition (OCR), and the same process applies when the patient has prescribed a further medical suggestion according to the test report. These models combine to form a comprehensive Decision Support System (DSS) for the diagnosis of liver disease.

In recent years, large language models (LLMs), empowered by massive text corpora and deep learning techniques, have demonstrated breakthrough advancements in cross-domain knowledge transfer and human-machine dialogue interactions [1]. Within the healthcare domain, LLMs are increasingly deployed across nine core application scenarios, including intelligent diagnosis, personalized treatment, and drug discovery, garnering significant attention from both academia and industry [2, 3]. A particularly important area of focus is the development and evaluation of Chinese medical LLMs, which face unique challenges due to the specialized nature of medical knowledge and the high-stakes implications of clinical decision-making. Hence, ensuring the reliability and safety of these models has become critical, necessitating rigorous evaluation frameworks [4]. Current research on medical LLMs evaluation exhibits two predominant trends. On one hand, general-domain benchmarks (e.g., HELM [5], MMLU [6]) assess foundational model capabilities through medical knowledge tests. On the other hand, specialized medical evaluation systems (e.g., MedQA [7], C-Eval-Medical [8]) emphasize clinical reasoning and ethical compliance. Notably, the MedBench framework [9], jointly developed by institutions including Shanghai AI Laboratory, has emerged as the most influential benchmark for Chinese medical LLMs. By establishing a standardized evaluation system spanning five dimensions--medical language comprehension, complex reasoning, and safety ethics--it has attracted participation from hundreds of research teams.

Speech technologies are transforming interactions across various sectors, from healthcare to call centers and robots, yet their performance on African-accented conversations remains underexplored. We introduce Afrispeech-Dialog, a benchmark dataset of 50 simulated medical and non-medical African-accented English conversations, designed to evaluate automatic speech recognition (ASR) and related technologies. We assess state-of-the-art (SOTA) speaker diarization and ASR systems on long-form, accented speech, comparing their performance with native accents and discover a 10%+ performance degradation. Additionally, we explore medical conversation summarization capabilities of large language models (LLMs) to demonstrate the impact of ASR errors on downstream medical summaries, providing insights into the challenges and opportunities for speech technologies in the Global South. Our work highlights the need for more inclusive datasets to advance conversational AI in low-resource settings.

Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data. We found that augmentation improves prognostic performance for datasets that: have fewer observations, with smaller baseline AUC, have higher cardinality categorical variables, and have more balanced outcome variables. No specific generative model consistently outperformed the others. We developed a decision support model that can be used to inform analysts if augmentation would be useful. For seven small application datasets, augmenting the existing data results in an increase in AUC between 4.31% (AUC from 0.71 to 0.75) and 43.23% (AUC from 0.51 to 0.73), with an average 15.55% relative improvement, demonstrating the nontrivial impact of augmentation on small datasets (p=0.0078). Augmentation AUC was higher than resampling only AUC (p=0.016). The diversity of augmented datasets was higher than the diversity of resampled datasets (p=0.046).

AI-empowered drug recommendation has become an important task in healthcare research areas, which offers an additional perspective to assist human doctors with more accurate and more efficient drug prescriptions. Generally, drug recommendation is based on patients' diagnosis results in the electronic health records. We assume that there are three key factors to be addressed in drug recommendation: 1) elimination of recommendation bias due to limitations of observable information, 2) better utilization of historical health condition and 3) coordination of multiple drugs to control safety. To this end, we propose DrugRec, a causal inference based drug recommendation model. The causal graphical model can identify and deconfound the recommendation bias with front-door adjustment.

In this short paper, we introduce work that is aiming to purposefully venture into this mesh of questions from a different starting point. Interjecting into the conversation, we want to ask: 'What is it to be skilled at work?' Building on work from scholars like Tim Ingold, and strands of longstanding research in workplace studies and CSCW, our interest is in turning the attention to the active work of 'being good', or 'being skilled', at what we as workers do. As we see it, skill provides a counterpoint to the version of intelligence that appears to be easily blackboxed in systems like Slack, and that ultimately reduces much of what people do to work well together. To put it slightly differently, skill - as we will argue below - gives us a way into thinking about work as a much more entangled endeavour, unfolding through multiple and interweaving sets of practices, places, tools and collaborations. In this vein, designing for the future of work seems to be about much more than where work is done or how we might bolt on discrete containers of intelligence. More fruitful would be attending to how we succeed in threading so many entities together to do our jobs well - in 'coming to be skilled'.

It's probably a good idea to keep your opinions to yourself if your friend gets a terrible new haircut - but soon you might not get a choice. That's because scientists at the University of Texas at Austin have trained an artificial intelligence (AI) to read a person's mind and turn their innermost thoughts into text. Three study participants listened to stories while lying in an MRI machine, while an AI'decoder' analysed their brain activity. They were then asked to read a different story or make up their own, and the decoder could then turn the MRI data into text in real time. The breakthrough raises concerns about'mental privacy' as it could be the first step in being able to eavesdrop on others' thoughts.