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.

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."

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.

In the healthcare sector, chatbots are essential since they quickly increase productivity. Chatbots provide several advantages in the healthcare sector, not just for professionals but also for patients. It is known that doctors usually make an effort to be accessible to their patients, but due to their busy schedules, it is occasionally impossible to accommodate everyone. Therefore, chatbots save the day by lightening the load on medical professionals. The use of AI chatbots is improving hospital patient care.

Helping to reduce costs and enhance productivity are both things that your employer will look kindly upon. But what if you use an external tool for those tasks and the tasks involve confidential data that ended up on a server outside of the control of your company? As a news writer at Tom's Hardware reported there were 3 incidents in 20 days where Samsung staff shared confidential information with ChatGPT. In other organizations, an executive cut and pasted their firm's 2023 strategy document into ChatGPT and asked it to create a PowerPoint deck, and a doctor submitted his patient's name and their medical condition and asked ChatGPT to craft a letter to the patient's insurance company. All of these actions were performed with the best of the organization in mind, but ended up taking confidential information outside of the company.

Background: Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to utilize machine learning to estimate probability of relapse in early-stage non-small-cell lung cancer patients? Methods: For predicting relapse in 1,387 early-stage (I-II), non-small-cell lung cancer (NSCLC) patients from the Spanish Lung Cancer Group data (65.7 average age, 24.8% females, 75.2% males) we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHAP local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. Results: Machine learning models trained on tabular data exhibit a 76% accuracy for the Random Forest model at predicting relapse evaluated with a 10-fold cross-validation (model was trained 10 times with different independent sets of patients in test, train and validation sets, the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a 200-patient, held-out test set, calibrated on a held-out set of 100 patients. Conclusions: Our results show that machine learning models trained on tabular and graph data can enable objective, personalised and reproducible prediction of relapse and therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer. Keywords: Non-Small-Cell Lung Cancer, Tumor Recurrence Prediction, Machine Learning

Based on the patient's 3D scan data, using AI technology, the crown prosthesis design that is most suitable for the patient's oral environment is generated in seconds. At the same time, it automatically identifies the margin line between the tooth and gum of the crown prosthesis to be manufactured and suggests an optimized margin line. Then, it goes through a process of creating an optimal crown design in a matter of seconds, considering the surrounding and antagonist teeth. Finally, the crown design may be downloaded in the desired file format (STL, OBJ, PLY, etc.) and sent to a 3D printer or milling machine for an immediate prosthesis production.

Computer vision is a domain of AI that enables the automatic assessment of images and videos. Many mobile banking apps use computer vision to coach customers to take photos of their checks for electronic deposits. If you hold the camera too far from the check or the lighting is too dark, the app will provide real-time advice about the required adjustments. The same could be done for user-submitted photos and videos in clinical trials; this approach has already been tested in telemedicine8. If a patient's entire body needs to be visible in the video, this could be confirmed automatically while the video is being captured in the trial's mobile application, allowing for immediate adjustments.