Paralysed man can feel objects through another person's hand Keith Thomas, a man in his 40s with no sensation or movement in his hands, is able to feel and move objects by controlling another person's hand via a brain implant. The technique might one day even allow us to experience another person's body over long distances. Keith Thomas (right) was able to control another person's hand A man with paralysis has been able to move and sense another person's hand as if it were his own, thanks to a new kind of "telepathic" brain implant. "We created a mind-body connection between two different individuals," says Chad Bouton at the Feinstein Institutes for Medical Research in New York state. The approach could be used as a form of rehabilitation after spinal cord injury, allowing people with paralysis to work together, and may one day even allow people to share experiences remotely, says Bouton.

Will robots routinely take cheek swabs in the future – and if so, how soon will this happen? Robotic pets that boost well-being. It is hard to predict the future with any certainty, but, as a biomedical reporter, I was curious to read a book that envisions how fast-evolving technology could transform healthcare. In Hacking Humanity: How technology can save your health, and your life, Lara Lewington draws on more than a decade of experience as a technology reporter at the BBC to cover an impressive array of innovations: from medical robots to lab-grown organs and genetic editing to treat certain conditions. "Let me show you the way to a future where we shall be hacking humanity," she writes in the introduction.

Machine learning (ML) methods are being increasingly used across various domains of medicine research. However, despite advancements in the use of ML in medicine, clear and definitive guidelines for determining sample sizes in medical ML research are lacking. This article proposes a method for determining sample sizes for medical research utilizing ML methods, beginning with the determination of the testing set sample size, followed with the determination of the training set and total sample sizes. Introduction Machine learning (ML) methods are being increasingly used in medical research, spanning various domains of medicine from oncology, orthopaedics, ophthalmology and general practice (Sirocchi et al., 2024). However, despite this advancement in medical research, currently there are no clear and definitive guidelines for determining sample sizes when using ML methods in the medical domain.

Despite progresses in data engineering, there are areas with limited consistencies across data validation and documentation procedures causing confusions and technical problems in research involving machine learning. There have been progresses by introducing frameworks like "Datasheets for Datasets", however there are areas for improvements to prepare datasets, ready for ML pipelines. Here, we extend the framework to "Datasheets for AI and medical datasets - DAIMS." Our publicly available solution, DAIMS, provides a checklist including data standardization requirements, a software tool to assist the process of the data preparation, an extended form for data documentation and pose research questions, a table as data dictionary, and a flowchart to suggest ML analyses to address the research questions. The checklist consists of 24 common data standardization requirements, where the tool checks and validate a subset of them. In addition, we provided a flowchart mapping research questions to suggested ML methods. DAIMS can serve as a reference for standardizing datasets and a roadmap for researchers aiming to apply effective ML techniques in their medical research endeavors. DAIMS is available on GitHub and as an online app to automate key aspects of dataset evaluation, facilitating efficient preparation of datasets for ML studies.

Research quality evaluation is important for departmental evaluations and academic career decisions. Unfortunately, the evaluators may not have time to fully read the work assessed and may instead rely on the reputation or Journal Impact Factor of the publishing journals, on the citation counts for individual articles, or on the reputation or career citations of the author. Whilst journal-based evidence is not optimal (Waltman & Traag, 2021), the main article-level indicator, citation counts, only directly reflects the scholarly impact of work and not its rigour, originality, and societal impacts (Aksnes, et al., 2019), all of which are relevant quality dimensions (Langfeldt et al., 2020). Moreover, article citation counts are ineffective for newer articles (Wang, 2013). In response, attempts to use Large Language Models (LLMs) to evaluate the quality of academic work have shown that ChatGPT quality scores are at least as effective as citation counts in most fields and substantially better in a few (Thelwall & Yaghi, 2024). Medicine is an exception, however, with ChatGPT research quality scores having a small negative correlation with the mean scores of the submitting department in the Research Excellence Framework (REF) Clinical Medicine Unit of Assessment (UoA) (Thelwall, 2024ab; Thelwall & Yaghi, 2024).

"Fargo" series creator Noah Hawley spoke with Fox News Digital at the Emmys, and warned that while he doesn't think AI can replicate human creativity, it still poses a threat. Noah Hawley attends the premiere of FOX's "Lucy In The Sky" at Darryl Zanuck Theater at FOX Studios on Sept. 25, 2019, in Los Angeles. READY FOR BATTLE: "Fargo" series creator Noah Hawley is wary of the good and bad in artificial intelligence. AI OPTIMISM: A prominent Silicon Valley businessman and venture capitalist believes artificial intelligence can spur deflation and create enough growth to help those whose jobs will be lost to the technology. MEDICAL MIRACLE: A New York man who was left paralyzed after a diving accident is starting to regain movement a year after receiving an artificial intelligence-powered implant in his brain.

A New York man who was left paralyzed after a diving accident is starting to regain movement a year after receiving an artificial intelligence-powered implant in his brain. A year ago, Keith Thomas, 46, was only able to move his arms an inch. Today, after the groundbreaking procedure, he is able to extend his arm, grasp a cup and take a drink using only his thoughts and stimulation. He has also regained more sensation in his wrist and arm, allowing him to feel the fur of his family's dog. In 2020, Thomas was living on Long Island and working as a trader on Wall Street when he experienced a diving accident that left him paralyzed from the chest down.

This personalized approach not only enhances the efficacy of treatments but also minimizes the risk of adverse effects (Agyeman and Ofori-Asenso, 2015; Kumari et al., 2023). However, the success of precision medicine heavily relies on the interpretation of complex, multidimensional data sets, where statistical analysis plays a pivotal role (Alyass et al., 2015). The integration of statistical methodologies in precision medicine is not just a mere addition but a fundamental necessity. Advanced statistical techniques enable the extraction of meaningful insights from large-scale genomics, proteomic, and metabolomic data, which are the cornerstone of precision medicine (Wafi and Mirnezami, 2018; Pinu et al., 2019). These methodologies include, but are not limited to, predictive modeling, machine learning algorithms, and complex data visualization techniques, all of which contribute to more accurate diagnosis, prognosis, and treatment planning (Bellazzi and Zupan, 2008; Davatzikos et al., 2018; Richter and Khoshgoftaar, 2018). The heterogeneity of data sources in precision medicine, ranging from electronic health records (EHRs) to high-throughput sequencing data, presents unique challenges in data integration and interpretation (Martinez-Garcia and Hernández-Lemus, 2022). Statistical analysis serves as a bridge, merging these diverse data types into coherent, interpretable information that can guide clinical decision-making. However, the field is not without its challenges. Issues such as overfitting, handling of highdimensional data, and maintaining the balance between model complexity and interpretability are ongoing areas of research (Bolón-Canedo et al., 2015; Xu et al., 2019; Bommert, 2020; Pes, 2020; Hou and Behdinan, 2022).

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Artificial intelligence (AI) is being applied across the healthcare spectrum -- from administration to patient interaction and medical research, diagnosis and treatment. Healthcare AI is the application of artificial intelligence to medical services and the administration or delivery of medical services. Machine learning (ML), large and often unstructured datasets, advanced sensors, natural language processing (NLP) and robotics are all being used in a growing number of healthcare sectors.

What do you achieve with deep learning? Deep learning is a part of our daily life. For example, when you upload a photo to Facebook, deep learning helps by automatically tagging your friends. If you use digital assistants like Siri, Cortana or Alexa, they serve you to the benefit with the help of natural language processing and speech recognition. When you meet with overseas customers on Skype, it translates in real time.