Researchers have proposed a new conceptual framework called “Health Elements” that positions digital technologies and AI as core structural drivers of health alongside biological, behavioral, social, and environmental factors. Published in Health Data Science, the framework argues that health outcomes emerge from dynamic interactions across multiple domains rather than from isolated risk factors alone. The study reflects growing recognition that digital systems—including algorithms, wearable devices, AI-enabled diagnostics, and health data infrastructures—are increasingly shaping health behaviors, access to care, and population-level outcomes.

The authors also discuss how multimodal health data integration, complex systems science, and AI-based analytical methods could support more adaptive public health and clinical decision-making. At the same time, they warn that algorithmic bias, digital inequity, and governance challenges may reinforce existing health disparities if ethical safeguards are not built into future digital health systems. An accompanying editorial describes the framework as a significant extension of traditional Social Determinants of Health models for the digital era.

Recently, the team led by Professor Jinzhong Ma and Tao Wang at Shanghai General Hospital, in collaboration with Professor Guoqing Pan at Jiangsu University, conducted a systematic review on this topic, comprehensively summarizing the latest advances and advancing the development and application of photodynamic antibacterial strategies for titanium implants.

A research paper by scientists from Beijing University of Posts and Telecommunications designed a multimodal controller termed the tactile, kinesthetic, and electromyography (EMG) bionic gripping controller (TKE-BGC).

The new research paper, published on May. 13 in the journal Cyborg and Bionic Systems, developed a bioinspired control framework that dramatically improves prosthetic hand stability during such dynamic tasks.

A research paper by scientists from Capital Medical University proposed a diffusion tensor imaging (DTI)-based DenseNet to identify SVCI from subcortical ischemic vascular disease (SIVD) and to profile multidomain cognitive risks.

The new research paper, published on May. 13 in the journal Cyborg and Bionic Systems, developed a deep‑learning framework that uses routine diffusion tensor imaging to both identify SVCI and profile an individual’s domain‑specific cognitive weaknesses.