Table Info

Table 1.

Publications in AI for wound assessment and healing (in chronological order)

Year	Location	Citation	Description of AI approach	Description of wound problem
2003	Trzaska, Ljubljana, Slovenia	[83]	Comprehensible evaluation of prognostic factors and prediction of wound healing	General
2020	Los Angeles, California, USA	[80]	Development of a model to predict healing of chronic wounds within 12 weeks	Chronic wounds
2021	Mineola, New York, USA	[74]	Development of a method for clinical evaluation of AI-based digital wound assessment tools	General
2021	Ann Arbor, Michigan, USA	[85]	Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes	Peri-implantitis
2022	Santa Cruz, California, USA	[76]	Automatic wound detection and size estimation using deep learning algorithms	General
2022	Toronto, Ontario, Canada	[77]	Fully automated wound tissue segmentation using deep learning on mobile devices: cohort study	General
2022	Pittsburgh, Pennsylvania, USA	[79]	Predicting chronic wound healing time using machine learning	Chronic wounds
2022	Sydney, New South Wales, Australia	[78]	Evaluation of an AI app to improve wound assessment and management amid the COVID-19 pandemic	General
2022	Halifax, Nova Scotia, Canada	[73]	Risk profiling in the prevention and treatment of chronic wounds using AI	Chronic wounds
2022	Melbourne, Victoria, Australia	[81]	Computerised prediction of healing for VLU	VLU
2022	Milwaukee, Wisconsin, USA	[63]	Image-based AI in wound assessment: a systematic review	General
2023	Toronto, Ontario, Canada	[84]	Towards an AI-based objective prognostic model for quantifying wound healing	General
2023	Karachi, Sindh, Pakistan	[82]	Diabetic wounds and AI: a mini-review	Diabetic wounds
2023	Tel Aviv, Israel	[86]	Application of AI methodologies to chronic wound care and management: a scoping review	Chronic wounds
2023	Hat Yai, Songkhla, Thailand	[75]	AI-assisted assessment of wound tissue with automatic color and measurement calibration on images taken with a smartphone	General

Declarations

Author contributions

DTPL: Writing—original draft. TDP: Conceptualization, Writing—original draft. Both authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

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86.

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