A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America
Abstract This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards iden...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01726-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850243436516474880 |
|---|---|
| author | Ariel Yuhan Ong Priyal Taribagil Mertcan Sevgi Aditya U. Kale Eliot R. Dow Trystan Macdonald Ashley Kras Gregory Maniatopoulos Xiaoxuan Liu Pearse A. Keane Alastair K. Denniston Henry David Jeffry Hogg |
| author_facet | Ariel Yuhan Ong Priyal Taribagil Mertcan Sevgi Aditya U. Kale Eliot R. Dow Trystan Macdonald Ashley Kras Gregory Maniatopoulos Xiaoxuan Liu Pearse A. Keane Alastair K. Denniston Henry David Jeffry Hogg |
| author_sort | Ariel Yuhan Ong |
| collection | DOAJ |
| description | Abstract This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards identifying best practice and areas for improvement. 36 AIaMDs from 28 manufacturers were identified – 97% (35/36) approved in the EU, 22% (8/36) in Australia, and 8% (3/36) in the USA. Most targeted diabetic retinopathy detection. 19% (7/36) did not have published evidence describing performance. For the remainder, 131 clinical evaluation studies (range 1-22/AIaMD) describing 192 datasets/cohorts were identified. Demographics were poorly reported (age recorded in 52%, sex 51%, ethnicity 21%). On a study-level, few included head-to-head comparisons against other AIaMDs (8%,10/131) or humans (22%, 29/131), and 37% (49/131) were conducted independently of the manufacturer. Only 11 studies (8%) were interventional. There is scope for expanding AIaMD applications to other ophthalmic imaging modalities, conditions, and use cases. Facilitating greater transparency from manufacturers, better dataset reporting, validation across diverse populations, and high-quality interventional studies with implementation-focused outcomes are key steps towards building user confidence and supporting clinical integration. |
| format | Article |
| id | doaj-art-9ebbe489803749348d22d613102ebb33 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-9ebbe489803749348d22d613102ebb332025-08-20T02:00:00ZengNature Portfolionpj Digital Medicine2398-63522025-05-018111310.1038/s41746-025-01726-8A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and AmericaAriel Yuhan Ong0Priyal Taribagil1Mertcan Sevgi2Aditya U. Kale3Eliot R. Dow4Trystan Macdonald5Ashley Kras6Gregory Maniatopoulos7Xiaoxuan Liu8Pearse A. Keane9Alastair K. Denniston10Henry David Jeffry Hogg11Moorfields Eye Hospital NHS Foundation TrustMoorfields Eye Hospital NHS Foundation TrustMoorfields Eye Hospital NHS Foundation TrustUniversity Hospitals Birmingham NHS Foundation TrustRetinal Consultants Medical GroupUniversity Hospitals Birmingham NHS Foundation TrustSydney Eye HospitalUniversity of LeicesterUniversity Hospitals Birmingham NHS Foundation TrustMoorfields Eye Hospital NHS Foundation TrustNIHR Moorfields Biomedical Research CentreMoorfields Eye Hospital NHS Foundation TrustAbstract This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards identifying best practice and areas for improvement. 36 AIaMDs from 28 manufacturers were identified – 97% (35/36) approved in the EU, 22% (8/36) in Australia, and 8% (3/36) in the USA. Most targeted diabetic retinopathy detection. 19% (7/36) did not have published evidence describing performance. For the remainder, 131 clinical evaluation studies (range 1-22/AIaMD) describing 192 datasets/cohorts were identified. Demographics were poorly reported (age recorded in 52%, sex 51%, ethnicity 21%). On a study-level, few included head-to-head comparisons against other AIaMDs (8%,10/131) or humans (22%, 29/131), and 37% (49/131) were conducted independently of the manufacturer. Only 11 studies (8%) were interventional. There is scope for expanding AIaMD applications to other ophthalmic imaging modalities, conditions, and use cases. Facilitating greater transparency from manufacturers, better dataset reporting, validation across diverse populations, and high-quality interventional studies with implementation-focused outcomes are key steps towards building user confidence and supporting clinical integration.https://doi.org/10.1038/s41746-025-01726-8 |
| spellingShingle | Ariel Yuhan Ong Priyal Taribagil Mertcan Sevgi Aditya U. Kale Eliot R. Dow Trystan Macdonald Ashley Kras Gregory Maniatopoulos Xiaoxuan Liu Pearse A. Keane Alastair K. Denniston Henry David Jeffry Hogg A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America npj Digital Medicine |
| title | A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America |
| title_full | A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America |
| title_fullStr | A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America |
| title_full_unstemmed | A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America |
| title_short | A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America |
| title_sort | scoping review of artificial intelligence as a medical device for ophthalmic image analysis in europe australia and america |
| url | https://doi.org/10.1038/s41746-025-01726-8 |
| work_keys_str_mv | AT arielyuhanong ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT priyaltaribagil ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT mertcansevgi ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT adityaukale ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT eliotrdow ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT trystanmacdonald ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT ashleykras ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT gregorymaniatopoulos ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT xiaoxuanliu ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT pearseakeane ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT alastairkdenniston ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT henrydavidjeffryhogg ascopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT arielyuhanong scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT priyaltaribagil scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT mertcansevgi scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT adityaukale scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT eliotrdow scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT trystanmacdonald scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT ashleykras scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT gregorymaniatopoulos scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT xiaoxuanliu scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT pearseakeane scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT alastairkdenniston scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica AT henrydavidjeffryhogg scopingreviewofartificialintelligenceasamedicaldeviceforophthalmicimageanalysisineuropeaustraliaandamerica |