Innovations in Computer Vision for External Disease Identification
DOI:
https://doi.org/10.38142/jisdb.v4i1.1624Keywords:
Computer VisionAbstract
The rapid development of artificial intelligence (AI) has introduced significant innovations in healthcare, particularly through computer vision technologies. This study explores qualitative perspectives on the use of computer vision for external disease identification, focusing on its applications, benefits, challenges, and future potential. A thematic review of literature from 2015 to 2025 was conducted using peer-reviewed journals, policy reports, and case studies. The findings reveal that computer vision has been successfully applied in dermatology, ophthalmology, and oral health, with performance often comparable to or exceeding that of general practitioners. Mobile and cloud-based applications extend these innovations to community healthcare, enabling wider access and patient empowerment. However, limitations remain, including dataset biases, privacy concerns, and the need for ethical frameworks. The study concludes that computer vision offers transformative opportunities for early and accessible disease detection, but its success depends on interdisciplinary collaboration, equitable dataset development, and responsible integration into healthcare systems. Recommendations for future research include longitudinal validation, cross-cultural testing, and integration with multimodal health data.
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Copyright (c) 2025 Nicholas RENALDO, Wilda SUSANTI , Rangga Rahmadian YULIENDI , Yulvia Nora MARLIM , Wahyu Joni KURNIAWAN, Gusrio TENDRA
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons Attribution-NonCommercial 4.0 International License.
