ReviewJuntendo medical journal2025
Data Science in Medical and Healthcare: Current Landscape.
Review in Juntendo medical journal, 2025. 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data science is revolutionizing various industries and its impact on healthcare and life sciences is particularly profound. The vast amounts of data generated in these fields present both opportunities and challenges, necessitating professionals to extract insights and create value from these data resources. However, effective data-driven solutions in healthcare require a unique combination of technical data science skills and deep-domain expertise in areas such as medicine, public health, and sports science. This review discusses the growing importance of domain knowledge in data science and the need for interdisciplinary professionals who can bridge the gap between data analysis and practical applications in the healthcare sector. Furthermore, this paper highlights specific applications of data science in healthcare and life sciences, leveraging artificial intelligence (AI) and advanced computational methods. By integrating cutting-edge data science techniques with profound domain understanding, these applications aim to drive innovation, advance medical research, improve patient outcomes, and deepen our understanding of human health and well-being. Overall, this review underscores the synergies between data science and domain expertise in healthcare and life sciences, emphasizing the importance of interdisciplinary collaboration in unlocking the full potential of data-driven solutions in these critical fields.
Indexed as
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.