{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T22:54:05Z","timestamp":1790808845095,"version":"4.1.0"},"reference-count":40,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IRCCS Humanitas Research Hospital"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Accurate intraocular lens (IOL) power calculation is vital for achieving the desired postoperative spherical equivalent (SE) in cataract surgery. Generative Artificial-Intelligence (AI) systems are increasingly being used in ophthalmology to refine diagnostic and surgical planning. However, it is still unknown whether a low-cost, easily accessible generative AI model like DeepSeek can match the accuracy of conventional biometric formulas. To evaluate the accuracy of DeepSeek, an open-source generative artificial intelligence (AI), in predicting postoperative refractive spherical equivalent compared to the Barrett Universal II formula in uncomplicated cataract surgeries. Methods: This study analyzed biometric data from 50 eyes of 50 patients who underwent cataract surgery between July 2024 and January 2025 at Humanitas Research Hospital in Milan, Italy. Only uncomplicated cases of emmetropia with Alcon AcrySof\u00ae SA60WF IOL implantation were included. 30\u201340 days postoperative subjective refraction was measured with a calibrated trial-frame and 6 m logMAR chart by an experienced optometrist. Prediction error (PE), median absolute error (MedAE), standard deviation (SD), and cumulative frequency of PE diopters range were calculated. A Wilcoxon signed-rank test was performed to assess statistical significance. Results: Barrett showed MedAE 0.36 D [0.16\u20130.64] and MAE 0.43 D (95% CI, 0.34\u20130.52) while DeepSeek-R1 showed MedAE 0.76 D [0.52\u20131.01] and MAE 0.77 D (95% CI, 0.67\u20130.87). Cumulative accuracy (AE threshold) at \u00b10.25\/\u00b10.50\/\u00b10.75\/\u00b11.00\/\u00b11.25\/\u00b11.50\/\u00b11.75 D was 37.7\/71.7\/81.1\/92.5\/100.0\/100.0\/100.0% for Barrett Universal II and 11.1\/25.9\/50.0\/74.1\/88.9\/96.3\/100.0% for DeepSeek-R1 (McNemar p &lt; 0.01 at each threshold). The paired comparison of per-eye absolute errors favored Barrett (Wilcoxon signed-rank test, p &lt; 0.0001). Conclusions: In this cohort, Barrett Universal II outperformed DeepSeek-R1 across MedAE\/MAE and cumulative accuracy thresholds, with a significant paired difference. A general-purpose generative model used off-the-shelf (fixed A-constant, no ophthalmology-specific tuning) did not match the accuracy of a validated vergence-based formula; established formulas remain the reference standard for clinical IOL power calculation.<\/jats:p>","DOI":"10.3390\/jcm14248870","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T09:57:24Z","timestamp":1765879044000},"page":"8870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Does DeepSeek Provide Clinically Acceptable Intraocular Lens (IOL) Power Predictions in Cataract Surgery? A Proof-of-Concept Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9213-2834","authenticated-orcid":false,"given":"Giovanni","family":"Ottonelli","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"De Rosa","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9049-2203","authenticated-orcid":false,"given":"Jacopo","family":"Celada Ballanti","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8714-4052","authenticated-orcid":false,"given":"Alessandro","family":"Gaeta","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Medical Specialties (DIMI), Universit\u00e0 di Genova, Viale Benedetto XV, 16132 Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3864-8246","authenticated-orcid":false,"given":"Paolo","family":"Vinciguerra","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy"},{"name":"Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4889-6315","authenticated-orcid":false,"given":"Alessandra","family":"Di Maria","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy"},{"name":"Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1111\/j.1755-3768.2007.00879.x","article-title":"Calculation of intraocular lens power: A review","volume":"85","author":"Olsen","year":"2007","journal-title":"Acta Ophthalmol. Scand."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.jcrs.2007.10.031","article-title":"Sources of error in intraocular lens power calculation","volume":"34","author":"Norrby","year":"2008","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"665","DOI":"10.3928\/1081597X-20110202-01","article-title":"Comparison of immersion ultrasound, partial coherence interferometry, and low coherence reflectometry for ocular biometry in cataract patients","volume":"27","author":"Carones","year":"2011","journal-title":"J. Refract. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Haigis, W., Hoffer, K.J., and Hoffer, K.J. (2024). The Haigis Formula. Intraocular Lens Calculations; Essentials in Ophthalmology, Springer.","DOI":"10.1007\/978-3-031-50666-6_41"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1016\/j.ophtha.2011.07.023","article-title":"Holladay 2 formula","volume":"118","author":"Ioannides","year":"2011","journal-title":"Ophthalmology"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Barrett, G.D. (2024). Barrett Formulas: Strategies to Improve IOL Power Prediction. Intraocular Lens Calculations; Essentials in Ophthalmology, Springer.","DOI":"10.1007\/978-3-031-50666-6_37"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1016\/j.jcrs.2011.05.042","article-title":"Optimizing intraocular lens power calculations in eyes with axial lengths above 25.0 mm","volume":"37","author":"Wang","year":"2011","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.jcrs.2008.06.029","article-title":"Intraocular lens calculation after refractive surgery for myopia: Haigis-L formula","volume":"34","author":"Haigis","year":"2008","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1016\/S0886-3350(01)01346-3","article-title":"Ray tracing for intraocular lens calculation","volume":"28","author":"Preussner","year":"2002","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s40662-021-00265-z","article-title":"Machine learning adaptation of intraocular lens power calculation for a patient group","volume":"8","author":"Mori","year":"2021","journal-title":"Eye Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1097\/ICU.0000000000001090","article-title":"Artificial intelligence applications in cataract and refractive surgeries","volume":"35","author":"Rampat","year":"2024","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1097\/j.jcrs.0000000000001182","article-title":"Predictability of the vault after implantable collamer lens implantation using OCT and artificial intelligence in White patient eyes","volume":"49","author":"Russo","year":"2023","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1097\/j.jcrs.0000000000001358","article-title":"Comment on: Efficacy of segmented axial length and artificial intelligence approaches to intraocular lens power calculation in short eyes","volume":"50","author":"Savini","year":"2024","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"ref_15","unstructured":"Bi, X., Chen, D., Chen, G., Chen, S., Dai, D., Deng, C., Ding, H., Dong, K., Du, Q., and Fu, Z. (2024). DeepSeek LLM: Scaling Open-Source Language Models with Longtermism. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101095","DOI":"10.1016\/j.xcrm.2023.101095","article-title":"Artificial intelligence in ophthalmology: The path to the real-world clinic","volume":"4","author":"Li","year":"2023","journal-title":"Cell Rep. Med."},{"key":"ref_18","unstructured":"(2025, March 11). DeepSeek Artificial Intelligence. Available online: https:\/\/deepseek.ai\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mondillo, G., Colosimo, S., Perrotta, A., Frattolillo, V., and Masino, M. (2025). Comparative Evaluation of Advanced AI Reasoning Models in Pediatric Clinical Decision Support: ChatGPT O1 vs. DeepSeek-R1. medRxiv.","DOI":"10.1101\/2025.01.27.25321169"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1002\/ksa.12628","article-title":"DeepSeek versus ChatGPT: Multimodal artificial intelligence revolutionizing scientific discovery. From language editing to autonomous content generation-Redefining innovation in research and practice","volume":"33","author":"Kayaalp","year":"2025","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_21","unstructured":"Qin, Z., Gui, Q., Bian, M., Wang, R., Ge, H., Yao, D., Sun, Z., Zhao, Y., Zhang, Y., and Shi, H. (2025). Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qiu, P., Wu, C., Liu, S., Zhao, W., Chen, Z., Gu, H., Peng, C., Zhang, Y., Wang, Y., and Xie, W. (2025). Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases. arXiv.","DOI":"10.1038\/s41467-025-64769-1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e115","DOI":"10.1016\/j.ophtha.2020.07.005","article-title":"Update on Intraocular Lens Power Calculation Study Protocols: The Better Way to Design and Report Clinical Trials","volume":"128","author":"Hoffer","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.jcrs.2016.07.021","article-title":"Intraocular lens power formula accuracy: Comparison of 7 formulas","volume":"42","author":"Kane","year":"2016","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3758\/BF03193146","article-title":"G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences","volume":"39","author":"Faul","year":"2007","journal-title":"Behav. Res. Methods"},{"key":"ref_26","unstructured":"Engineering Heidelberg (2025, March 11). ANTERION\u2014Multimodal Imaging Platform for the Anterior Segment. Available online: https:\/\/business-lounge.heidelbergengineering.com\/us\/en\/products\/anterion\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1016\/j.jcrs.2016.06.029","article-title":"Comparison of 9 intraocular lens power calculation formulas","volume":"42","author":"Cooke","year":"2016","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1038\/s41433-023-02906-0","article-title":"Reliability and accuracy of artificial intelligence ChatGPT in providing information on ophthalmic diseases and management to patients","volume":"38","author":"Cappellani","year":"2024","journal-title":"Eye"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_30","first-page":"e79221","article-title":"DeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier","volume":"17","author":"Temsah","year":"2025","journal-title":"Cureus"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Betzler, B.K., Rim, T.H., Sabanayagam, C., and Cheng, C.Y. (2022). Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Front. Digit. Health, 4.","DOI":"10.3389\/fdgth.2022.889445"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1167\/tvst.9.2.13","article-title":"Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology","volume":"9","author":"Lin","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40662-024-00384-3","article-title":"Prognostic potentials of AI in ophthalmology: Systemic disease forecasting via retinal imaging","volume":"11","author":"Tan","year":"2024","journal-title":"Eye Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mikhail, A.D., Farah, A., Milad, J., Nassrallah, W., Mihalache, A., Milad, D., Antaki, F., Balas, M., Popovic, M.M., and Feo, A. (2025). Performance of DeepSeek-R1 in Ophthalmology: An Evaluation of Clinical Decision-Making and Cost-Effectiveness. medRxiv, preprint.","DOI":"10.1101\/2025.02.10.25322041"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100184","DOI":"10.1016\/j.mcpdig.2024.11.005","article-title":"Fine-Tuning Large Language Models for Specialized Use Cases","volume":"3","author":"Anisuzzaman","year":"2025","journal-title":"Mayo Clin. Proc. Digit. Health"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ajo.2025.02.010","article-title":"Intraocular Lens Power Calculation\u2014Comparing Big Data Approaches to Established Formulas","volume":"273","author":"Redden","year":"2025","journal-title":"Am. J. Ophthalmol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1055\/a-1298-8121","article-title":"Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power","volume":"237","author":"Langenbucher","year":"2020","journal-title":"Klin. Monbl Augenheilkd."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e36609","DOI":"10.1016\/j.heliyon.2024.e36609","article-title":"IOL power calculation in long eyes: Selection of the best axial length adjustement factor using the most common formulas","volume":"10","author":"Cione","year":"2024","journal-title":"Heliyon"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cione, F., De Bernardo, M., Di Stasi, M., De Luca, M., Albano, R., and Rosa, N. (2024). Lens Factor Choice in IOL Power Calculation after Laser Refractive Surgery: The Right Constant for Advanced Lens Measurement Approach (ALMA). J. Clin. Med., 13.","DOI":"10.3390\/jcm13175186"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"150","DOI":"10.4103\/kjo.kjo_54_19","article-title":"Role of artificial intelligence and machine learning in ophthalmology","volume":"31","author":"Akkara","year":"2019","journal-title":"Kerala J. Ophthalmol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/24\/8870\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T10:15:39Z","timestamp":1765880139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/24\/8870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":40,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["jcm14248870"],"URL":"https:\/\/doi.org\/10.3390\/jcm14248870","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,15]]}}}