Evidence map›Paper›PMID 32395545›Full record

ReviewAnnals of translational medicine2020

Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters.

Federico Cabitza, Andrea Campagner, Clara Balsano

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  17. Balancing between A and I.Acta oncologica (Stockholm, Sweden) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Federico CabitzaDipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy.
Andrea CampagnerIRCCS Istituto Ortopedico Galeazzi, Milano, Italy.
Clara BalsanoDipartimento di Medicina Clinica, Sanità Pubblica, Scienze della Vita e dell'Ambiente, Università degli Studi dell'Aquila, L'Aquila, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interest in the application of machine learning (ML) techniques to medicine is growing fast and wide because of their ability to endow decision support systems with so-called artificial intelligence, particularly in those medical disciplines that extensively rely on digital imaging. Nonetheless, achieving a pragmatic and ecological validation of medical AI systems in real-world settings is difficult, even when these systems exhibit very high accuracy in laboratory settings. This difficulty has been called the "last mile of implementation." In this review of the concept, we claim that this metaphorical mile presents two chasms: the hiatus of human trust and the hiatus of machine experience. The former hiatus encompasses all that can hinder the concrete use of AI at the point of care, including availability and usability issues, but also the contradictory phenomena of cognitive ergonomics, such as automation bias (overreliance on technology) and prejudice against the machine (clearly the opposite). The latter hiatus, on the other hand, relates to the production and availability of a sufficient amount of reliable and accurate clinical data that is suitable to be the "experience" with which a machine can be trained. In briefly reviewing the existing literature, we focus on this latter hiatus of the last mile, as it has been largely neglected by both ML developers and doctors. In doing so, we argue that efforts to cross this chasm require data governance practices and a focus on data work, including the practices of data awareness and data hygiene. To address the challenge of bridging the chasms in the last mile of medical AI implementation, we discuss the six main socio-technical challenges that must be overcome in order to build robust bridges and deploy potentially effective AI in real-world clinical settings.

Indexed as

Artificial intelligencedata hygieneDevOpslast mile

Identifiers

PMID32395545
PMCPMC7210125

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.