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| 2. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 3. |
- Gama, Fábio, Ass. Professor, 1980-, et al.
(författare)
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Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice : Scoping Review
- 2022
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Ingår i: Journal of Medical Internet Research. - Toronto, ON : JMIR Publications. - 1438-8871. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.Conclusions: This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science. ©Fábio Gama, Daniel Tyskbo, Jens Nygren, James Barlow, Julie Reed, Petra Svedberg.
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| 4. |
- Gerkin, RC, et al.
(författare)
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The best COVID-19 predictor is recent smell loss: a cross-sectional study
- 2020
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundCOVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.MethodsThis preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.ResultsBoth C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.ConclusionsAs smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.
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| 6. |
- Petersson, Lena, 1968-, et al.
(författare)
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Challenges to implementing artificial intelligence in healthcare : a qualitative interview study with healthcare leaders in Sweden
- 2022
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Ingår i: BMC Health Services Research. - London : BioMed Central (BMC). - 1472-6963. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) for healthcare presents potential solutions to some of the challenges faced by health systems around the world. However, it is well established in implementation and innovation research that novel technologies are often resisted by healthcare leaders, which contributes to their slow and variable uptake. Although research on various stakeholders’ perspectives on AI implementation has been undertaken, very few studies have investigated leaders’ perspectives on the issue of AI implementation in healthcare. It is essential to understand the perspectives of healthcare leaders, because they have a key role in the implementation process of new technologies in healthcare. The aim of this study was to explore challenges perceived by leaders in a regional Swedish healthcare setting concerning the implementation of AI in healthcare.Methods: The study takes an explorative qualitative approach. Individual, semi-structured interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders. The analysis was performed using qualitative content analysis, with an inductive approach.Results: The analysis yielded three categories, representing three types of challenge perceived to be linked with the implementation of AI in healthcare: 1) Conditions external to the healthcare system; 2) Capacity for strategic change management; 3) Transformation of healthcare professions and healthcare practice.Conclusions: In conclusion, healthcare leaders highlighted several implementation challenges in relation to AI within and beyond the healthcare system in general and their organisations in particular. The challenges comprised conditions external to the healthcare system, internal capacity for strategic change management, along with transformation of healthcare professions and healthcare practice. The results point to the need to develop implementation strategies across healthcare organisations to address challenges to AI-specific capacity building. Laws and policies are needed to regulate the design and execution of effective AI implementation strategies. There is a need to invest time and resources in implementation processes, with collaboration across healthcare, county councils, and industry partnerships. © The Author(s) 2022.
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| 7. |
- Staedele, Veronika, et al.
(författare)
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The complex Y-chromosomal history of gorillas
- 2022
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Ingår i: American Journal of Primatology. - : John Wiley & Sons. - 0275-2565 .- 1098-2345. ; 84:3
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Tidskriftsartikel (refereegranskat)abstract
- Studies of the evolutionary relationships among gorilla populations using autosomal and mitochondrial sequences suggest that male-mediated gene flow may have been important in the past, but data on the Y-chromosomal relationships among the gorilla subspecies are limited. Here, we genotyped blood and noninvasively collected fecal samples from 12 captives and 257 wild male gorillas of known origin representing all four subspecies (Gorilla gorilla gorilla, G. g. diehli, G. beringei beringei, and G. b. graueri) at 10 Y-linked microsatellite loci resulting in 102 unique Y-haplotypes for 224 individuals. We found that western lowland gorilla (G. g. gorilla) haplotypes were consistently more diverse than any other subspecies for all measures of diversity and comprised several genetically distinct groups. However, these did not correspond to geographical proximity and some closely related haplotypes were found several hundred kilometers apart. Similarly, our broad sampling of eastern gorillas revealed that mountain (G. b. beringei) and Grauer's (G. b. graueri) gorilla Y-chromosomal haplotypes did not form distinct clusters. These observations suggest structure in the ancestral population with subsequent mixing of differentiated haplotypes by male dispersal for western lowland gorillas, and postisolation migration or incomplete lineage sorting due to short divergence times for eastern gorillas.
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| 8. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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