Google AI Ultra

G. Pagliai, M. Dinu, M. P. Madarena, M. Bonaccio, L. Iacoviello, F. Sofi

Abstract Increasing evidence suggests that high consumption of ultra-processed foods (UPF) is associated with an increase in non-communicable diseases, overweight and obesity. The present study systematically reviewed all observational studies that investigated the association between UPF consumption and health status. A comprehensive search of MEDLINE, Embase, Scopus, Web of Science and Google Scholar was conducted, and reference lists of included articles were checked. Only cross-sectional and prospective cohort studies were included. At the end of the selection process, twenty-three studies (ten cross-sectional and thirteen prospective cohort studies) were included in the systematic review. As regards the cross-sectional studies, the highest UPF consumption was associated with a significant increase in the risk of overweight/obesity (+39 %), high waist circumference (+39 %), low HDL-cholesterol levels (+102 %) and the metabolic syndrome (+79 %), while no significant associations with hypertension, hyperglycaemia or hypertriacylglycerolaemia were observed. For prospective cohort studies evaluating a total population of 183 491 participants followed for a period ranging from 3·5 to 19 years, highest UPF consumption was found to be associated with increased risk of all-cause mortality in five studies (risk ratio (RR) 1·25, 95 % CI 1·14, 1·37; P < 0·00001), increased risk of CVD in three studies (RR 1·29, 95 % CI 1·12, 1·48; P = 0·0003), cerebrovascular disease in two studies (RR 1·34, 95 % CI 1·07, 1·68; P = 0·01) and depression in two studies (RR 1·20, 95 % CI 1·03, 1·40; P = 0·02). In conclusion, increased UPF consumption was associated, although in a limited number of studies, with a worse cardiometabolic risk profile and a higher risk of CVD, cerebrovascular disease, depression and all-cause mortality.

Mahmud Omar, Saleh Nassar, Kareem Hijazi, Benjamin S. Glicksberg, Girish N. Nadkarni, Eyal Klang

BACKGROUND Amidst the increasing use of AI in medical research, this study specifically aims to assess and compare the accuracy and credibility of openAI's GPT-4 and Google's Gemini in their ability to generate medical research introductions, focusing on the precision and reliability of their citations across five medical fields. METHODS We compared the two models, OpenAI's GPT-4 and Google's Gemini Ultra, across five medical fields, focusing on the credibility and accuracy of citations, alongside the analysis of introduction length and unreferenced data. RESULTS Gemini outperformed GPT-4 in reference precision. Gemini's references showed 77.2 % correctness and 68.0 % accuracy, compared to GPT-4's 54.0 % correctness and 49.2 % accuracy (p < 0.001 for both). This 23.2 percentage point difference in correctness and 18.8 in accuracy represents an improvement in citation reliability. GPT-4 generated longer introductions (332.4 ± 52.1 words vs. Gemini's 256.4 ± 39.1 words, p < 0.001) but included more unreferenced facts and assumptions (1.6 ± 1.2 vs. 1.2 ± 1.06 instances, p = 0.001). CONCLUSION While Gemini demonstrates significantly superior performance in generating credible and accurate references for medical research introductions, both models produced fabricated evidence, limiting their reliability for reference searching. This snapshot comparison of two prominent AI models highlights the potential and limitations of AI in academic content creation. The findings underscore the critical need for verification of AI-generated academic content and call for ongoing research into evolving AI models and their applications in scientific writing.

Pietro Bonazzi, Thomas Rüegg, Sizhen Bian, Yawei Li, Michele Magno

Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency efficiency due to the typically heavy computational load they impose on edge platforms. This work leverages one of the first “Artificial Intelligence (AI) in sensor” vision platforms, IMX500 by Sony, to achieve ultra- fast and ultra-low-power end-to-end edge vision applications. We evaluate the IMX500 and compare it to other edge platforms, such as the Google Coral Dev Micro and Sony Spresense, by exploring gaze estimation as a case study. We propose TinyTracker, a highly efficient, fully quantized model for 2D gaze estimation designed to maximize the performance of the edge vision systems considered in this study. TinyTracker achieves a 41x size reduction (~ 600Kb) compared to iTracker [1] without significant loss in gaze estimation accuracy (maximum of 0.16 cm when fully quantized). TinyTracker's deployment on the Sony IMX500 vision sensor results in end-to-end latency of around 19ms. The camera takes around 17.9ms to read, process and transmit the pixels to the accelerator. The inference time of the network is 0.86ms with an additional 0.24 ms for retrieving the results from the sensor. The overall energy consumption of the end-to-end system is 4.9 mJ, including 0.06 mJ for inference. The end-to-end study shows that IMX500 is 1.7x faster than Coral Micro (19ms vs 34.4ms) and 7x more power efficient (4.9mJ VS 34.2mJ).

L. Puce, H. Ceylan, C. Trompetto, Filippo Cotellessa, Cristina Schenone, Lucio Marinelli, Piotr Żmijewski, N. Bragazzi, L. Mori

Nutrition is vital for athletic performance, especially in ultra-endurance sports, which pose unique nutritional challenges. Despite its importance, there exist gaps in the nutrition knowledge among athletes, and emerging digital tools could potentially bridge this gap. The ULTRA-Q, a sports nutrition questionnaire adapted for ultra-endurance athletes, was used to assess the nutritional knowledge of ChatGPT-3.5, ChatGPT-4, Google Bard, and Microsoft Copilot. Their performance was compared with experienced ultra-endurance athletes, registered sports nutritionists and dietitians, and the general population. ChatGPT-4 demonstrated the highest accuracy (93%), followed by Microsoft Copilot (92%), Bard (84%), and ChatGPT-3.5 (83%). The averaged AI model achieved an overall score of 88%, with the highest score in Body Composition (94%) and the lowest in Nutrients (84%). The averaged AI model outperformed the general population by 31% points and ultra-endurance athletes by 20% points in overall knowledge. The AI model exhibited superior knowledge in Fluids, outperforming registered dietitians by 49% points, the general population by 42% points, and ultra-endurance athletes by 32% points. In Body Composition, the AI model surpassed the general population by 31% points and ultraendurance athletes by 24% points. In Supplements, it outperformed registered dietitians by 58% points and the general population by 55% points. Finally, in Nutrients and in Recovery, it outperformed the general population only, by 24% and 29% points, respectively. AI models show high proficiency in sports nutrition knowledge, potentially serving as valuable tools for nutritional education and advice. AI-generated insights could be integrated with expert human judgment for effective athlete performance optimization.

C. Gagliardo, P. Feraco, Eleonora Contrino, C. D'angelo, Laura Geraci, G. Salvaggio, A. Gagliardo, L. La Grutta, M. Midiri, Maurizio Marrale

Ultra-low-field magnetic resonance imaging (ULF-MRI), operating below 0.2 Tesla, is gaining renewed interest as a re-emerging diagnostic modality in a field dominated by high- and ultra-high-field systems. Recent advances in magnet design, RF coils, pulse sequences, and AI-based reconstruction have significantly enhanced image quality, mitigating traditional limitations such as low signal- and contrast-to-noise ratio and reduced spatial resolution. ULF-MRI offers distinct advantages: reduced susceptibility artifacts, safer imaging in patients with metallic implants, low power consumption, and true portability for point-of-care use. This narrative review synthesizes the physical foundations, technological advances, and emerging clinical applications of ULF-MRI. A focused literature search across PubMed, Scopus, IEEE Xplore, and Google Scholar was conducted up to August 11, 2025, using combined keywords targeting hardware, software, and clinical domains. Inclusion emphasized scientific rigor and thematic relevance. A comparative analysis with other imaging modalities highlights the specific niche ULF-MRI occupies within the broader diagnostic landscape. Future directions and challenges for clinical translation are explored. In a world increasingly polarized between the push for ultra-high-field excellence and the need for accessible imaging, ULF-MRI embodies a modern “David versus Goliath” theme, offering a sustainable, democratizing force capable of expanding MRI access to anyone, anywhere.