2022
DOI: 10.1111/iwj.13952
|View full text |Cite
|
Sign up to set email alerts
|

Risk profiling in the prevention and treatment of chronic wounds using artificial intelligence

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
13
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 12 publications
(13 citation statements)
references
References 31 publications
0
13
0
Order By: Relevance
“…In the editorials, I discussed how it was helping create and drive data driven wound care practices. This was expanded upon in a recent editorial by Cross and Harding 3 …”
mentioning
confidence: 94%
See 3 more Smart Citations
“…In the editorials, I discussed how it was helping create and drive data driven wound care practices. This was expanded upon in a recent editorial by Cross and Harding 3 …”
mentioning
confidence: 94%
“…Wound care, is perhaps one of the most data rich areas because of its interaction with or relationship to multiple human comorbidities. AI is certainly going to revolutionise this data management and aid with both assessment and treatment approaches 3 . Generative AI, however, can help understand this complexity and provide “accurate” summaries of data and outcomes.…”
mentioning
confidence: 99%
See 2 more Smart Citations
“…Several factors can influence this inconsistency in practice: with currently around only 40% of wound care specialists regularly using assessment tools, poor patient outcomes routinely result due to this inconsistency 3 ; with over 25% of wounds having no recorded differential diagnosis 4 ; with prolonged referral times, often resulting in a breakdown in continuity of care 5 ; with a lack of risk profiling due to infrequent and inconsistent care 6 ; with a lack of training and the emerging global health practitioner shortage 7 , 8 ; …”
mentioning
confidence: 99%