Literature DB >> 26469375

Collective intelligence for translational medicine: Crowdsourcing insights and innovation from an interdisciplinary biomedical research community.

Eleanor Jane Budge1, Sandra Maria Tsoti2, Daniel James Howgate3, Shivan Sivakumar4,5, Morteza Jalali6,7,8.   

Abstract

Translational medicine bridges the gap between discoveries in biomedical science and their safe and effective clinical application. Despite the gross opportunity afforded by modern research for unparalleled advances in this field, the process of translation remains protracted. Efforts to expedite science translation have included the facilitation of interdisciplinary collaboration within both academic and clinical environments in order to generate integrated working platforms fuelling the sharing of knowledge, expertise, and tools to align biomedical research with clinical need. However, barriers to scientific translation remain, and further progress is urgently required. Collective intelligence and crowdsourcing applications offer the potential for global online networks, allowing connection and collaboration between a wide variety of fields. This would drive the alignment of biomedical science with biotechnology, clinical need, and patient experience, in order to deliver evidence-based innovation which can revolutionize medical care worldwide. Here we discuss the critical steps towards implementing collective intelligence in translational medicine using the experience of those in other fields of science and public health.

Entities:  

Keywords:  Biomedical research; collective intelligence; crowdsourcing; interdisciplinary collaboration; research networks; translational medicine

Mesh:

Year:  2015        PMID: 26469375     DOI: 10.3109/07853890.2015.1091945

Source DB:  PubMed          Journal:  Ann Med        ISSN: 0785-3890            Impact factor:   4.709


  4 in total

1.  Translational researchers' training and development needs, preferences, and barriers: A survey in a National Institute for Health Research Biomedical Research Centre in the United Kingdom.

Authors:  Karen Bell; Syed Ghulam Sarwar Shah; Lorna R Henderson; Vasiliki Kiparoglou
Journal:  Clin Transl Sci       Date:  2022-05-15       Impact factor: 4.438

2.  Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Authors:  Andreas Holzinger
Journal:  Brain Inform       Date:  2016-03-02

3.  A Hybrid In Silico and Tumor-on-a-Chip Approach to Model Targeted Protein Behavior in 3D Microenvironments.

Authors:  Valentina Palacio-Castañeda; Simon Dumas; Philipp Albrecht; Thijmen J Wijgers; Stéphanie Descroix; Wouter P R Verdurmen
Journal:  Cancers (Basel)       Date:  2021-05-18       Impact factor: 6.639

Review 4.  Crowdsourcing in health and medical research: a systematic review.

Authors:  Cheng Wang; Larry Han; Gabriella Stein; Suzanne Day; Cedric Bien-Gund; Allison Mathews; Jason J Ong; Pei-Zhen Zhao; Shu-Fang Wei; Jennifer Walker; Roger Chou; Amy Lee; Angela Chen; Barry Bayus; Joseph D Tucker
Journal:  Infect Dis Poverty       Date:  2020-01-20       Impact factor: 4.520

  4 in total

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