Applications of Artificial Intelligence for Facial Aesthetic Surgery: a Systematic Review
Main Article Content
Artificial intelligence (AI) offers opportunities to improve assessment, visualization, and communication in facial aesthetic surgery; however, its clinical reliability remains uncertain. This study aimed to synthesize contemporary evidence on AI applications, performance, methodological quality, and clinical relevance in facial aesthetic surgery. A systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines searched PubMed/MEDLINE, SAGE Journals, ScienceDirect, EBSCO, Scopus, and Web of Science for English-language original studies published between January 2022 and August 2026. Eleven studies met the eligibility criteria and were included in the qualitative synthesis and risk-of-bias assessment using an adapted framework informed by the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. AI applications included apparent-age estimation, automated facial morphometry, rhinoplasty simulation, patient counseling, and social media analysis. AI-based systems quantified postoperative changes in perceived age and facial morphology, generated realistic surgical visualizations, and provided comprehensible patient education responses. However, visual realism did not establish patient-specific predictive accuracy, and improvements in computational performance did not necessarily translate into greater patient satisfaction or psychosocial benefits. Six studies raised some concerns regarding risk of bias, while five were judged to have a high risk of bias. Methodological heterogeneity, limited external validation, proprietary algorithms, and potential demographic biases limited the interpretation and generalizability of the findings. AI represents a promising adjunct to facial aesthetic surgery; however, prospective multicenter validation, transparent reporting, demographically diverse datasets, and patient-centered outcome assessment are needed before routine clinical integration can be recommended with confidence.
Keywords:
artificial intelligence, facial aesthetic surgery, plastic surgery, generative artificial intelligence, computer vision
Abdali, H. M., Ayoub, A. Y., Omer, R. A., Alanazi, R. A., Alsaedi, B. S., Almutairi, L. B., Alhakami, B. A., Alfifa, A. Y., Alnasser, N. A., & Hakami, A. H. (2025). Applications of artificial intelligence in oral and maxillofacial cosmetic surgery: A systematic review of diagnostic, planning, and outcome assessment tools. Cureus, 17(9).
Al-Dhubaibi, M. S., Mohammed, G. F., Atef, L. M., Bahaj, S. S., Al-Dhubaibi, A. M., & Bukhari, A. M. (2025). Artificial intelligence in aesthetic medicine: Applications, challenges, and future directions. Journal of Cosmetic Dermatology, 24(6), Article e70241.
Atiyeh, B., Emsieh, S., Hakim, C., & Chalhoub, R. (2023). A narrative review of artificial intelligence (AI) for objective assessment of aesthetic endpoints in plastic surgery. Aesthetic Plastic Surgery. https://doi.org/10.1007/s00266-023-03328-9
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194.
Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2), Article 125. https://doi.org/10.3390/healthcare12020125
Bhowmik, R. T., Kandathil, C. K., & Most, S. P. (2023). Automating the standardized cosmesis and health nasal outcomes survey classification with convolutional neural networks. Facial Plastic Surgery & Aesthetic Medicine, 25(6), 487–493.
Borsting, E., et al. (2020). Applied deep learning in plastic surgery: Classifying rhinoplasty with a mobile application. Plastic and Reconstructive Surgery, 145(1), 194e–200e.
Chiou, T.-W., Yen, C.-I., Hsiao, Y.-C., & Chen, H.-C. (2024). AI prediction for post-lower blepharoplasty age reduction. Aesthetic Surgery Journal, 44(12), NP922–NP930. https://doi.org/10.1093/asj/sjae182
Choi, E., Leonard, K. W., Jassal, J. S., et al. (2023). Artificial intelligence in facial plastic surgery: A review of current applications, future applications, and ethical considerations. Facial Plastic Surgery, 39(5), 454–459.
Copeland-Halperin, L. R. (2026). Artificial intelligence in plastic surgery: Current status, limitations, and future directions. Aesthetic Surgery Journal, 46(2), 113–121.
Dumitrascu, D. I., Popa, S. L., Incze, V., Amarie, D. S., Gaspari, L., Aluas, P., Ismaiel, A., Leucuta, D. C., David, L., Mihaileanu, F. V., & Gherman, C. D. (2026). Artificial intelligence and esthetics: Redefining precision and beauty in plastic surgery. Medicina, 62(4), Article 633.
Duong, T. V., Vy, V. P., & Hung, T. N. (2024). Artificial intelligence in plastic surgery: Advancements, applications, and future. Cosmetics, 11(4), Article 109.
Elliott, Z. T., Bheemreddy, A., Fiorella, M., Martin, A. M., Christopher, V., Krein, H., et al. (2023). Artificial intelligence for objectively measuring years regained after facial rejuvenation surgery. American Journal of Otolaryngology, 44(2), Article 103775. https://doi.org/10.1016/j.amjoto.2022.103775
Er Bilir, A., & Ero?ul, Ö. (2026). Artificial intelligence-assisted quantitative evaluation of upper eyelid blepharoplasty outcomes in dermatochalasis: Methodological validation and clinical correlation. BMC Ophthalmology, 26(1), Article 162. https://doi.org/10.1186/s12886-026-04693-0
Flynn, J. P., Cha, E. L., Flynn, T. J., et al. (2021). Machine learning generated synthetic faces for use in facial aesthetic research. Facial Plastic Surgery & Aesthetic Medicine, 23(5), 339–343.
Fuzi, J., Meller, C., Ch'ng, S., et al. (2023). The emerging role of artificial intelligence tools for outcome measurement in facial reanimation surgery: A review. Facial Plastic Surgery & Aesthetic Medicine, 25(6), 556–561.
Goodyear, K., Saffari, P. S., Esfandiari, M., Baugh, S., Rootman, D. B., & Karlin, J. N. (2023). Estimating apparent age using artificial intelligence: Quantifying the effect of blepharoplasty. Journal of Plastic, Reconstructive & Aesthetic Surgery, 85, 336–343. https://doi.org/10.1016/j.bjps.2023.07.017
Han, S. S., Kim, M. S., Lim, W., Park, G. H., Park, I., & Chang, S. E. (2018). Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology, 138(7), 1529–1538. https://doi.org/10.1016/j.jid.2018.01.028
Hebel, N. S. D., Boonipat, T., Lin, J., Shapiro, D., & Bite, U. (2023). Artificial intelligence in surgical evaluation: A study of facial rejuvenation techniques. Aesthetic Surgery Journal Open Forum, 5, Article ojad032. https://doi.org/10.1093/asjof/ojad032
Kambham, A., Nguyen, K., Moghal, M., Masi, B., Matsui, C., De la Cruz Ku, G., et al. (2026). Artificial intelligence-enhanced evaluation of blepharoplasty content on social media: A cross-sectional study. Journal of Cutaneous and Aesthetic Surgery. Advance online publication. https://doi.org/10.25259/JCAS_257_2025
Knoedler, S., Alfertshofer, M., Simon, S., Panayi, A. C., Saadoun, R., Palackic, A., et al. (2024). Turn your vision into reality—AI-powered pre-operative outcome simulation in rhinoplasty surgery. Aesthetic Plastic Surgery, 48(23), 4833–4838. https://doi.org/10.1007/s00266-024-04043-9
Kreh, C. C., Roider, L., Firouzbakht, P. K., Nathan, C., Prada, C. A., Lund, H. G., Jr., et al. (2025). Artificial intelligence analysis of periorbital rejuvenation. Aesthetic Surgery Journal, 45(2), 215–220. https://doi.org/10.1093/asj/sjae201
Landau, M., Kassir, M., & Goldust, M. (2025). Transforming aesthetic medicine with generative artificial intelligence. Journal of Cosmetic Dermatology, 24(2).
Lim, B., Seth, I., Kah, S., Sofiadellis, F., Ross, R. J., Rozen, W. M., et al. (2023). Using generative artificial intelligence tools in cosmetic surgery: A study on rhinoplasty, facelifts, and blepharoplasty procedures. Journal of Clinical Medicine, 12(20), Article 6524. https://doi.org/10.3390/jcm12206524
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
Marusza, M., Krupski, A., Stielow, V., & Zatyka, A. (2026). Digital misinformation in aesthetic dermatology: The role of social media and generative artificial intelligence in shaping patient safety, trust, and evidence-based practice: A narrative review (2022–2026). Quality in Sport, 53, Article 70151.
Nogueira, R., Eguchi, M., Kasmirski, J., de Lima, B. V., Dimatos, D. C., Lima, D. L., Glatter, R., Tran, D. L., & Piccinini, P. S. (2025). Machine learning, deep learning, artificial intelligence and aesthetic plastic surgery: A qualitative systematic review. Aesthetic Plastic Surgery, 49(1), 389–399.
Ozmen, B. B., & Schwarz, G. S. (2025). Critical evaluation of generative text-to-image AI models in plastic surgery: Balancing promise and precision. Aesthetic Surgery Journal, 45(9), NP183–NP184.
Qin, F., & Gu, J. (2023). Artificial intelligence in plastic surgery: Current developments and future perspectives. Plastic and Aesthetic Research, 10(1).
Rokhshad, R., Keyhan, S. O., & Yousefi, P. (2023). Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: A narrative review. Maxillofacial Plastic and Reconstructive Surgery, 45(1), Article 14.
Segura-Bermudez, J. R., Rezwan, S., Barkhordarzadeh, A. D., Tahir, A. F., Khetpal, S., Zhao, T., et al. (2026). Aesthetically ideal noses created using a single artificial intelligence model: Validating literature and exploring ethnic differences. JPRAS Open, 49, 517–528. https://doi.org/10.1016/j.jpra.2026.03.003
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., et al. (2019). Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Medical Informatics, 7(3), Article e10010. https://doi.org/10.2196/10010
Shiraishi, M., Tanigawa, K., Tomioka, Y., Miyakuni, A., Moriwaki, Y., Yang, R., Oba, J., & Okazaki, M. (2024). Blepharoptosis consultation with artificial intelligence: Aesthetic surgery advice and counseling from Chat Generative Pre-Trained Transformer (ChatGPT). Aesthetic Plastic Surgery, 48(11), 2057–2063.
Shoham, G., Naveh, S., Confino, I., Zoabi, T., Govrin, O., Fliss, E., & Barnea, Y. (2026). A systematic review of applications, challenges, and future trajectories of artificial intelligence in cosmetic surgery. Aesthetic Surgery Journal, 46(2), 150–159.
Stephanian, B., Karki, S., Debnath, K., Saltychev, M., Rossi-Meyer, M., Kandathil, C. K., & Most, S. P. (2024). Role of artificial intelligence and machine learning in facial aesthetic surgery: A systematic review. Facial Plastic Surgery & Aesthetic Medicine, 26(6), 679–705.
Sultan, M. T., Sanan, M., Wenhui, L., & Li, G. (2026). Accuracy of descriptive and illustrative capacity of generative artificial intelligence in aesthetic surgery: A study of accelerated facial aging in depressed medical residents. Aesthetic Plastic Surgery. Advance online publication.
Tokgöz, E., & Carro, M. A. (2023). Applications of artificial intelligence, machine learning, and deep learning on facial plastic surgeries. In Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical engineering and science concepts (pp. 281–306). Springer Nature Switzerland.
Trabilsy, M., Genovese, A., Prabha, S., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Tao, C., & Forte, A. J. (2025). Utilizing generative text-to-image artificial intelligence models to explore race, gender, and age in plastic and aesthetic surgery. Aesthetic Surgery Journal, 45(9), NP172–NP178.
Xie, Y., Seth, I., Hunter-Smith, D. J., Rozen, W. M., Ross, R., & Lee, M. (2023). Aesthetic surgery advice and counseling from artificial intelligence: A rhinoplasty consultation with ChatGPT. Aesthetic Plastic Surgery, 47, 1985–1993. https://doi.org/10.1007/s00266-023-03338-7
Yassa, A., Akhavan, A., Ayad, S., Ayad, O., Colon, A., & Ignatiuk, A. (2025). Facial aesthetics in artificial intelligence: First investigation comparing results in a generative AI study. Eplasty, 25, Article e13.
Al-Dhubaibi, M. S., Mohammed, G. F., Atef, L. M., Bahaj, S. S., Al-Dhubaibi, A. M., & Bukhari, A. M. (2025). Artificial intelligence in aesthetic medicine: Applications, challenges, and future directions. Journal of Cosmetic Dermatology, 24(6), Article e70241.
Atiyeh, B., Emsieh, S., Hakim, C., & Chalhoub, R. (2023). A narrative review of artificial intelligence (AI) for objective assessment of aesthetic endpoints in plastic surgery. Aesthetic Plastic Surgery. https://doi.org/10.1007/s00266-023-03328-9
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194.
Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2), Article 125. https://doi.org/10.3390/healthcare12020125
Bhowmik, R. T., Kandathil, C. K., & Most, S. P. (2023). Automating the standardized cosmesis and health nasal outcomes survey classification with convolutional neural networks. Facial Plastic Surgery & Aesthetic Medicine, 25(6), 487–493.
Borsting, E., et al. (2020). Applied deep learning in plastic surgery: Classifying rhinoplasty with a mobile application. Plastic and Reconstructive Surgery, 145(1), 194e–200e.
Chiou, T.-W., Yen, C.-I., Hsiao, Y.-C., & Chen, H.-C. (2024). AI prediction for post-lower blepharoplasty age reduction. Aesthetic Surgery Journal, 44(12), NP922–NP930. https://doi.org/10.1093/asj/sjae182
Choi, E., Leonard, K. W., Jassal, J. S., et al. (2023). Artificial intelligence in facial plastic surgery: A review of current applications, future applications, and ethical considerations. Facial Plastic Surgery, 39(5), 454–459.
Copeland-Halperin, L. R. (2026). Artificial intelligence in plastic surgery: Current status, limitations, and future directions. Aesthetic Surgery Journal, 46(2), 113–121.
Dumitrascu, D. I., Popa, S. L., Incze, V., Amarie, D. S., Gaspari, L., Aluas, P., Ismaiel, A., Leucuta, D. C., David, L., Mihaileanu, F. V., & Gherman, C. D. (2026). Artificial intelligence and esthetics: Redefining precision and beauty in plastic surgery. Medicina, 62(4), Article 633.
Duong, T. V., Vy, V. P., & Hung, T. N. (2024). Artificial intelligence in plastic surgery: Advancements, applications, and future. Cosmetics, 11(4), Article 109.
Elliott, Z. T., Bheemreddy, A., Fiorella, M., Martin, A. M., Christopher, V., Krein, H., et al. (2023). Artificial intelligence for objectively measuring years regained after facial rejuvenation surgery. American Journal of Otolaryngology, 44(2), Article 103775. https://doi.org/10.1016/j.amjoto.2022.103775
Er Bilir, A., & Ero?ul, Ö. (2026). Artificial intelligence-assisted quantitative evaluation of upper eyelid blepharoplasty outcomes in dermatochalasis: Methodological validation and clinical correlation. BMC Ophthalmology, 26(1), Article 162. https://doi.org/10.1186/s12886-026-04693-0
Flynn, J. P., Cha, E. L., Flynn, T. J., et al. (2021). Machine learning generated synthetic faces for use in facial aesthetic research. Facial Plastic Surgery & Aesthetic Medicine, 23(5), 339–343.
Fuzi, J., Meller, C., Ch'ng, S., et al. (2023). The emerging role of artificial intelligence tools for outcome measurement in facial reanimation surgery: A review. Facial Plastic Surgery & Aesthetic Medicine, 25(6), 556–561.
Goodyear, K., Saffari, P. S., Esfandiari, M., Baugh, S., Rootman, D. B., & Karlin, J. N. (2023). Estimating apparent age using artificial intelligence: Quantifying the effect of blepharoplasty. Journal of Plastic, Reconstructive & Aesthetic Surgery, 85, 336–343. https://doi.org/10.1016/j.bjps.2023.07.017
Han, S. S., Kim, M. S., Lim, W., Park, G. H., Park, I., & Chang, S. E. (2018). Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology, 138(7), 1529–1538. https://doi.org/10.1016/j.jid.2018.01.028
Hebel, N. S. D., Boonipat, T., Lin, J., Shapiro, D., & Bite, U. (2023). Artificial intelligence in surgical evaluation: A study of facial rejuvenation techniques. Aesthetic Surgery Journal Open Forum, 5, Article ojad032. https://doi.org/10.1093/asjof/ojad032
Kambham, A., Nguyen, K., Moghal, M., Masi, B., Matsui, C., De la Cruz Ku, G., et al. (2026). Artificial intelligence-enhanced evaluation of blepharoplasty content on social media: A cross-sectional study. Journal of Cutaneous and Aesthetic Surgery. Advance online publication. https://doi.org/10.25259/JCAS_257_2025
Knoedler, S., Alfertshofer, M., Simon, S., Panayi, A. C., Saadoun, R., Palackic, A., et al. (2024). Turn your vision into reality—AI-powered pre-operative outcome simulation in rhinoplasty surgery. Aesthetic Plastic Surgery, 48(23), 4833–4838. https://doi.org/10.1007/s00266-024-04043-9
Kreh, C. C., Roider, L., Firouzbakht, P. K., Nathan, C., Prada, C. A., Lund, H. G., Jr., et al. (2025). Artificial intelligence analysis of periorbital rejuvenation. Aesthetic Surgery Journal, 45(2), 215–220. https://doi.org/10.1093/asj/sjae201
Landau, M., Kassir, M., & Goldust, M. (2025). Transforming aesthetic medicine with generative artificial intelligence. Journal of Cosmetic Dermatology, 24(2).
Lim, B., Seth, I., Kah, S., Sofiadellis, F., Ross, R. J., Rozen, W. M., et al. (2023). Using generative artificial intelligence tools in cosmetic surgery: A study on rhinoplasty, facelifts, and blepharoplasty procedures. Journal of Clinical Medicine, 12(20), Article 6524. https://doi.org/10.3390/jcm12206524
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
Marusza, M., Krupski, A., Stielow, V., & Zatyka, A. (2026). Digital misinformation in aesthetic dermatology: The role of social media and generative artificial intelligence in shaping patient safety, trust, and evidence-based practice: A narrative review (2022–2026). Quality in Sport, 53, Article 70151.
Nogueira, R., Eguchi, M., Kasmirski, J., de Lima, B. V., Dimatos, D. C., Lima, D. L., Glatter, R., Tran, D. L., & Piccinini, P. S. (2025). Machine learning, deep learning, artificial intelligence and aesthetic plastic surgery: A qualitative systematic review. Aesthetic Plastic Surgery, 49(1), 389–399.
Ozmen, B. B., & Schwarz, G. S. (2025). Critical evaluation of generative text-to-image AI models in plastic surgery: Balancing promise and precision. Aesthetic Surgery Journal, 45(9), NP183–NP184.
Qin, F., & Gu, J. (2023). Artificial intelligence in plastic surgery: Current developments and future perspectives. Plastic and Aesthetic Research, 10(1).
Rokhshad, R., Keyhan, S. O., & Yousefi, P. (2023). Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: A narrative review. Maxillofacial Plastic and Reconstructive Surgery, 45(1), Article 14.
Segura-Bermudez, J. R., Rezwan, S., Barkhordarzadeh, A. D., Tahir, A. F., Khetpal, S., Zhao, T., et al. (2026). Aesthetically ideal noses created using a single artificial intelligence model: Validating literature and exploring ethnic differences. JPRAS Open, 49, 517–528. https://doi.org/10.1016/j.jpra.2026.03.003
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., et al. (2019). Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Medical Informatics, 7(3), Article e10010. https://doi.org/10.2196/10010
Shiraishi, M., Tanigawa, K., Tomioka, Y., Miyakuni, A., Moriwaki, Y., Yang, R., Oba, J., & Okazaki, M. (2024). Blepharoptosis consultation with artificial intelligence: Aesthetic surgery advice and counseling from Chat Generative Pre-Trained Transformer (ChatGPT). Aesthetic Plastic Surgery, 48(11), 2057–2063.
Shoham, G., Naveh, S., Confino, I., Zoabi, T., Govrin, O., Fliss, E., & Barnea, Y. (2026). A systematic review of applications, challenges, and future trajectories of artificial intelligence in cosmetic surgery. Aesthetic Surgery Journal, 46(2), 150–159.
Stephanian, B., Karki, S., Debnath, K., Saltychev, M., Rossi-Meyer, M., Kandathil, C. K., & Most, S. P. (2024). Role of artificial intelligence and machine learning in facial aesthetic surgery: A systematic review. Facial Plastic Surgery & Aesthetic Medicine, 26(6), 679–705.
Sultan, M. T., Sanan, M., Wenhui, L., & Li, G. (2026). Accuracy of descriptive and illustrative capacity of generative artificial intelligence in aesthetic surgery: A study of accelerated facial aging in depressed medical residents. Aesthetic Plastic Surgery. Advance online publication.
Tokgöz, E., & Carro, M. A. (2023). Applications of artificial intelligence, machine learning, and deep learning on facial plastic surgeries. In Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical engineering and science concepts (pp. 281–306). Springer Nature Switzerland.
Trabilsy, M., Genovese, A., Prabha, S., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Tao, C., & Forte, A. J. (2025). Utilizing generative text-to-image artificial intelligence models to explore race, gender, and age in plastic and aesthetic surgery. Aesthetic Surgery Journal, 45(9), NP172–NP178.
Xie, Y., Seth, I., Hunter-Smith, D. J., Rozen, W. M., Ross, R., & Lee, M. (2023). Aesthetic surgery advice and counseling from artificial intelligence: A rhinoplasty consultation with ChatGPT. Aesthetic Plastic Surgery, 47, 1985–1993. https://doi.org/10.1007/s00266-023-03338-7
Yassa, A., Akhavan, A., Ayad, S., Ayad, O., Colon, A., & Ignatiuk, A. (2025). Facial aesthetics in artificial intelligence: First investigation comparing results in a generative AI study. Eplasty, 25, Article e13.
