ReviewHealth data science2026
Medical AI across Data Regimes to Promote Proactive Health.
Review in Health data science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
importanceProactive health has emerged as a transformative paradigm in modern public health, shifting the traditional emphasis from episodic, reactive care toward continuous, anticipatory, and preventive health management. This shift is both timely and necessary in the context of rising chronic disease burdens, aging populations, and increasing demands on healthcare systems. Understanding how advances in medical data and artificial intelligence (AI) underpin this transition is essential for guiding future research and practice. HIGHLIGHTS: This review synthesizes the evolution of proactive health alongside major developments in medical data regimes and AI technologies. The progression of medical AI is characterized across 4 data regimes, including sparse-data, small-data, big-data, and the emerging full-data era characterized by large-scale multimodal data integration. In tandem, AI methodologies have advanced from early expert systems built on rule-based knowledge engineering to contemporary large multimodal models capable of unifying diverse healthcare data streams. These technological and data-centric breakthroughs are reshaping proactive health practices across multiple domains, from decentralized health monitoring, risk-adaptive screening, dynamic and responsive treatment, virtual rehabilitation and digital health intervention, to construction of integrated medical and health services.
conclusionsDespite these advancements, marked technical and ethical challenges remain, limiting the translation of proactive health into routine clinical practice. Addressing these issues will be essential for realizing the full potential of proactive health and enabling future public health systems that are more anticipatory, efficient, and equitable.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.