Interdisciplinaridad: un requerimiento esencial para la traslación de investigación en radiómica a la práctica clínica

Sollini, Martina; Gelardi, Fabrizia; Pini, Cristiano; Bolton, Roberto C. Delgado; Chiti, Arturo; Kirienko, Margarita

doi:10.1016/j.remn.2019.10.003

Cited by 13 publications

(2 citation statements)

References 31 publications

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“…Radiomic and AI (i.e., image mining) (Sollini et al 2019a ) research benefits from an interdisciplinary attitude. Overall, the chance to exchange knowledge between experts in different fields (physicians and imagers, physicists and statisticians, biologists, informatics, engineers, and data mining) remarkably impacts on methodology (i.e., quality) and confers robustness to results and ultimately promoting image mining toward clinical practice (Sollini et al 2020b ).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Sollini

Bartoli

Marciano

et al. 2020

European J Hybrid Imaging

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Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer—being one of the most extensively malignancy studied by hybrid medical imaging—has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Sollini

Bartoli

Marciano

et al. 2020

European J Hybrid Imaging

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“…As such, basic research articles in the field typically acknowledge the need for biocompatibility considerations in future research. However, they show little or no evidence of meaningful interdisciplinary involvement from downstream regulatory or user areas (such as regulatory specialists, clinicians) that have been shown to significantly improve technological translatability (Datta Burton et al, 2020a;Patel et al, 2019;Sollini et al, 2020). To borrow from Sharp and Langer (2011, p. 527), 'convergence' of disciplines "where engineers and physical scientists are equal partners with biologists and clinicians in addressing many of the new medical challenges" has been at best limited in nanorobotics as expertise have stayed within their disciplinary silos.…”

Section: Where Next: Contextualising Bio-ai Within Its Environmentmentioning

confidence: 99%

Not Anytime Soon: The Clinical Translation of Nanorobots and Its Biocompatibility-Interdisciplinarity Critique

Burton

2022

Social and Cultural Studies of Robots and AI

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Medical Nanorobots: The Biocompatibility Constraints of Clinical Translation

Burton

2022

Biosystems &Amp; Biorobotics

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Interdisciplinaridad: un requerimiento esencial para la traslación de investigación en radiómica a la práctica clínica

Cited by 13 publications

References 31 publications

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Not Anytime Soon: The Clinical Translation of Nanorobots and Its Biocompatibility-Interdisciplinarity Critique

Medical Nanorobots: The Biocompatibility Constraints of Clinical Translation

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