Artificial Intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment, and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging, and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration, and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis, and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.
The intrusion of wet muck and fines and the potential of mud rushes pose safety risks for workers, equipment and infrastructure at El Teniente. Wet muck can also result in the loss of reserves because of the need to close drawpoints when large amounts of fine materials and moisture are observed. This paper presents the analysis and the development of a mathematical model to estimate wet muck entry for long-term planning applications at El Teniente. The models have been imbedded in BCRisk®, which is a machine-learning software that estimates hazards associated with the extraction process for underground mines. Four basins of El Teniente were included in the study of wet muck control: North, Center, South, and Reno. Each basin has mines with different characteristics in each exploitation sector. Consequently, models were built for each of the basins to represent its distinct reality.Several variables were investigated to define which determine the phenomenon. The variables include tonnage extracted or draw rate, amount of water entering the cave, season of the year, presence of mud in neighbouring drawpoints or sectors that have been closed due to wet muck above, and changes in surface or depressions. In addition, flow variables such as fragmentation and lithologies have been included and estimated with FlowSim 6.3® for increased precision. Results indicate that the classification models can reproduce the phenomenon with an acceptable precision of 71% and an average tonnage error per drawpoint of 7 to 10%. These results have been useful for long-term planning at El Teniente mine to predict wet muck entry and define when and where autonomous LHD may be required for the extraction of wet muck in the future.
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