{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T22:56:39Z","timestamp":1772060199221,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["DP2EB035858"],"award-info":[{"award-number":["DP2EB035858"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01LM014342"],"award-info":[{"award-number":["R01LM014342"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01LM013364"],"award-info":[{"award-number":["R01LM013364"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Stanford Center for Digital Health"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human-in-the-loop AI diagnostics entered the pediatric market as the first medical device for clinically precise autism diagnosis at scale, while fully automated deep learning approaches have remained underdeveloped. However, the importance of early autism detection, ideally before 3 years of age, underscores the value of developing even more automated AI approaches, due to their potentials for scale, reach, and privacy. We present the first systematic evaluation of multimodal LLMs as direct replacements for human annotation in AI-based autism detection. Evaluating seven Gemini model variants (1.5\u20132.5 series) on 50 YouTube videos shows clear generational progression: version 1.5 models achieve 72\u201380% accuracy, version 2.0 models reach 80%, and version 2.5 models attain 85\u201390%, with the best model (2.5 Pro) achieving 89.6% classification accuracy using validated autism detection AI models (LR5)\u2014comparable to the 88% clinical baseline and approaching crowdworker performance of 92\u201398%. The 24% improvement across two generations suggests the gap is closing. LLMs demonstrate high within-model consistency versus moderate human agreement, with distinct assessment strategies: LLMs focus on language\/behavioral markers, crowdworkers prioritize social-emotional engagement, clinicians balance both. While LLMs have yet to match the highest-performing subset of human annotators in their ability to extract behavioral features that are useful for human-in-the-loop AI diagnosis, their rapid improvement and advantages in consistency, scalability, cost, and privacy position them as potentially viable alternatives for aiding diagnostic processes in the future.<\/jats:p>","DOI":"10.3390\/a18110687","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T03:44:39Z","timestamp":1761795879000},"page":"687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6975-4953","authenticated-orcid":false,"given":"Parnian","family":"Azizian","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5778-6054","authenticated-orcid":false,"given":"Mohammadmahdi","family":"Honarmand","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1367-818X","authenticated-orcid":false,"given":"Aditi","family":"Jaiswal","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0077-5485","authenticated-orcid":false,"given":"Aaron","family":"Kline","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA"},{"name":"Department of Pediatrics (Clinical Informatics), Stanford University School of Medicine, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4423-5269","authenticated-orcid":false,"given":"Kaitlyn","family":"Dunlap","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA"},{"name":"Department of Pediatrics (Clinical Informatics), Stanford University School of Medicine, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3276-4411","authenticated-orcid":false,"given":"Peter","family":"Washington","sequence":"additional","affiliation":[{"name":"Department of Medicine (Clinical Informatics and Digital Transformation), University of California San Francisco, San Francisco, CA 94143, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7889-9146","authenticated-orcid":false,"given":"Dennis P.","family":"Wall","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA"},{"name":"Department of Pediatrics (Clinical Informatics), Stanford University School of Medicine, Stanford, CA 94305, USA"},{"name":"Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1023\/A:1005592401947","article-title":"The Autism Diagnostic Observation Schedule\u2014Generic: A standard measure of social and communication deficits associated with the spectrum of autism","volume":"30","author":"Lord","year":"2000","journal-title":"J. Autism Dev. Disord."},{"key":"ref_2","unstructured":"Lord, C., Rutter, M., DiLavore, P., Risi, S., Gotham, K., and Bishop, S. (2012). Autism Diagnostic Observation Schedule, Western Psychological Services. [2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/BF02172145","article-title":"Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders","volume":"24","author":"Lord","year":"1994","journal-title":"J. Autism Dev. Disord."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15585\/mmwr.ss7402a1","article-title":"Prevalence and early identification of autism spectrum disorder among children aged 4 and 8 years\u2014Autism and Developmental Disabilities Monitoring Network, 16 Sites, United States, 2022","volume":"74","author":"Shaw","year":"2025","journal-title":"MMWR Surveill. Summ."},{"key":"ref_5","unstructured":"National Autism Data Center (2025). How Early Does Diagnosis Happen? Autism by the Numbers, National Autism Data Center."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.2105\/AJPH.2017.304032","article-title":"Autism spectrum disorder among US children (2002\u20132010): Socioeconomic, racial, and ethnic disparities","volume":"107","author":"Durkin","year":"2017","journal-title":"Am. J. Public Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1352\/1934-9556-51.3.141","article-title":"Access to diagnosis and treatment services among Latino children with autism spectrum disorders","volume":"51","author":"Lopez","year":"2013","journal-title":"Intellect. Dev. Disabil."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1017\/S0954579408000370","article-title":"Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder","volume":"20","author":"Dawson","year":"2008","journal-title":"Dev. Psychopathol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1080\/09540261.2018.1432574","article-title":"Efficacy of early interventions for infants and young children with, and at risk for, autism spectrum disorders","volume":"30","author":"Landa","year":"2018","journal-title":"Int. Rev. Psychiatry"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tariq, Q., Daniels, J., Schwartz, J.N., Washington, P., Kalantarian, H., and Wall, D.P. (2018). Mobile detection of autism through machine learning on home video: A development and prospective validation study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002705"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wall, D.P., Dally, R., Luyster, R., Jung, J.Y., and DeLuca, T.F. (2012). Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0043855"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e100","DOI":"10.1038\/tp.2012.10","article-title":"Use of machine learning to shorten observation-based screening and diagnosis of autism","volume":"2","author":"Wall","year":"2012","journal-title":"Transl. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s13229-017-0180-6","article-title":"Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism","volume":"8","author":"Levy","year":"2017","journal-title":"Mol. Autism"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e514","DOI":"10.1038\/tp.2015.7","article-title":"Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning","volume":"5","author":"Kosmicki","year":"2015","journal-title":"Transl. Psychiatry"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1093\/jamia\/ocy039","article-title":"Machine learning approach for early detection of autism by combining questionnaire and home video screening","volume":"25","author":"Abbas","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fusaro, V.A., Daniels, J., Duda, M., DeLuca, T.F., D\u2019Angelo, O., Tamburello, J., Maniscalco, J., and Wall, D.P. (2014). The potential of accelerating early detection of autism through content analysis of YouTube videos. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093533"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Washington, P., Tariq, Q., Leblanc, E., Chrisman, B., Dunlap, K., Kline, A., Kalantarian, H., Penev, Y., Paskov, K., and Voss, C. (2021). Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-87059-4"},{"key":"ref_18","unstructured":"Washington, P., Leblanc, E., Dunlap, K., Penev, Y., Varma, M., Jung, J.Y., Chrisman, B., Sun, M.W., Stockham, N., and Paskov, K.M. (2021, January 5\u20137). Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder. Proceedings of the Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, Virtual."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Washington, P., Leblanc, E., Dunlap, K., Penev, Y., Kline, A., Paskov, K., Sun, M.W., Chrisman, B., Stockham, N., and Varma, M. (2020). Precision telemedicine through crowdsourced machine learning: Testing variability of crowd workers for video-based autism feature recognition. J. Pers. Med., 10.","DOI":"10.3390\/jpm10030086"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e51138","DOI":"10.2196\/51138","article-title":"A perspective on crowdsourcing and human-in-the-loop workflows in precision health","volume":"26","author":"Washington","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Washington, P., Kline, A., Mutlu, O.C., Leblanc, E., Hou, C., Stockham, N., Paskov, K., Chrisman, B., and Wall, D. (2021, January 8\u201313). Activity recognition with moving cameras and few training examples: Applications for detection of autism-related headbanging. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan.","DOI":"10.1145\/3411763.3451701"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Voss, C., Washington, P., Haber, N., Kline, A., Daniels, J., Fazel, A., De, T., McCarthy, B., Feinstein, C., and Winograd, T. (2016, January 12\u201316). Superpower glass: Delivering unobtrusive real-time social cues in wearable systems. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968310"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s41666-018-0034-9","article-title":"Guess what? Towards understanding autism from structured video using facial affect","volume":"3","author":"Kalantarian","year":"2019","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Serna-Aguilera, M., Nguyen, X.B., Singh, A., Rockers, L., Park, S.W., Neely, L., Seo, H.S., and Luu, K. (2024, January 3\u20135). Video-based autism detection with deep learning. Proceedings of the 2024 IEEE Green Technologies Conference (GreenTech), Springdale, AR, USA.","DOI":"10.1109\/GreenTech58819.2024.10520462"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kojovic, N., Natraj, S., Mohanty, S.P., Maillart, T., and Schaer, M. (2021). Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-94378-z"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, W., Cheng, M., Pan, Y., Yuan, L., Hu, S., Li, M., and Zeng, S. (2023, January 13\u201315). Assessing the social skills of children with autism spectrum disorder via language-image pre-training models. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Xiamen, China.","DOI":"10.1007\/978-981-99-8558-6_22"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TMM.2024.3521838","article-title":"Hear me, see me, understand me: Audio-visual autism behavior recognition","volume":"27","author":"Deng","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.procs.2020.03.399","article-title":"Analysis and detection of autism spectrum disorder using machine learning techniques","volume":"167","author":"Raj","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zunino, A., Morerio, P., Cavallo, A., Ansuini, C., Podda, J., Battaglia, F., Veneselli, E., Becchio, C., and Murino, V. (2018, January 20\u201324). Video gesture analysis for autism spectrum disorder detection. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545095"},{"key":"ref_30","unstructured":"Berlin S, J., Pandian, D., Rajagopalan, S.S., and Jayagopi, D. (2022, January 16\u201319). Detecting a child\u2019s stimming behaviours for autism spectrum disorder diagnosis using rgbpose-slowfast network. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7186762","DOI":"10.1155\/2018\/7186762","article-title":"A novel deep learning approach for recognizing stereotypical motor movements within and across subjects on the autism spectrum disorder","volume":"2018","author":"Sadouk","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhu, F.L., Wang, S.H., Liu, W.B., Zhu, H.L., Li, M., and Zou, X.B. (2023). A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name. Front. Psychiatry, 14.","DOI":"10.3389\/fpsyt.2023.1039293"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Eslami, T., Mirjalili, V., Fong, A., Laird, A.R., and Saeed, F. (2019). ASD-DiagNet: A hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front. Neuroinform., 13.","DOI":"10.3389\/fninf.2019.00070"},{"key":"ref_34","first-page":"23716","article-title":"Flamingo: A visual language model for few-shot learning","volume":"35","author":"Alayrac","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","unstructured":"Team, G., Anil, R., Borgeaud, S., Alayrac, J.B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A.M., Hauth, A., and Millican, K. (2023). Gemini: A family of highly capable multimodal models. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115667","DOI":"10.1016\/j.psychres.2023.115667","article-title":"Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment","volume":"333","author":"Kjell","year":"2024","journal-title":"Psychiatry Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fang, Z., Lin, W., Jiang, Y., Jin, W., Balaji, P., Wang, J., and Xia, T. (2025). Evaluation of large language models on mental health: From knowledge test to illness diagnosis. Front. Psychiatry, 16.","DOI":"10.3389\/fpsyt.2025.1646974"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s44184-024-00056-z","article-title":"Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation","volume":"3","author":"Stade","year":"2024","journal-title":"npj Ment. Health Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1016\/j.cell.2025.02.025","article-title":"Large language models deconstruct the clinical intuition behind diagnosing autism","volume":"188","author":"Stanley","year":"2025","journal-title":"Cell"},{"key":"ref_40","unstructured":"Jiang, Y., Shen, Q., Lai, S., Qi, S., Zheng, Q., Yao, L., Wang, Y., and Pan, G. (2024). Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs. arXiv."},{"key":"ref_41","first-page":"4359726","article-title":"Exploiting ChatGPT for diagnosing autism-associated language disorders and identifying distinct features","volume":"3","author":"Hu","year":"2024","journal-title":"Res. Sq."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e2429229","DOI":"10.1001\/jamanetworkopen.2024.29229","article-title":"Machine learning prediction of autism spectrum disorder from a minimal set of medical and background information","volume":"7","author":"Rajagopalan","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1002\/aur.2030","article-title":"The diagnosis conundrum: Comparison of crowdsourced and expert assessments of toddlers with high and low risk of autism spectrum disorder","volume":"11","author":"Myers","year":"2018","journal-title":"Autism Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Leblanc, E., Washington, P., Varma, M., Dunlap, K., Penev, Y., Kline, A., and Wall, D.P. (2020). Feature replacement methods enable reliable home video analysis for machine learning detection of autism. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-76874-w"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/aur.2226","article-title":"Screening for autism spectrum disorder in a naturalistic home setting using the systematic observation of red flags (SORF) at 18\u201324 months","volume":"13","author":"Dow","year":"2020","journal-title":"Autism Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s40249-020-0622-9","article-title":"Crowdsourcing in health and medical research: A systematic review","volume":"9","author":"Wang","year":"2020","journal-title":"Infect. Dis. Poverty"},{"key":"ref_47","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning transferable visual models from natural language supervision. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_48","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., and Anadkat, S. (2023). Gpt-4 technical report. arXiv."},{"key":"ref_49","unstructured":"Google AI (2025, September 09). Gemini Thinking Mode Documentation. Available online: https:\/\/ai.google.dev\/gemini-api\/docs\/thinking-mode."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/s41746-025-01985-5","article-title":"Evaluating the performance of general purpose large language models in identifying human facial emotions","volume":"8","author":"Nelson","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_51","unstructured":"Deng, C., Lai, S., Zhou, C., Bao, M., Yan, J., Li, H., Yao, L., and Wang, Y. (2024). ASD-Chat: An innovative dialogue intervention system for children with autism based on llm and vb-mapp. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, X.Y., Chen, Y.M., Chen, C.P., Su, B.H., Gau, S.S.F., and Lee, C.C. (2025, January 6\u201311). SocialRecNet: A Multimodal LLM-Based Framework for Assessing Social Reciprocity in Autism Spectrum Disorder. Proceedings of the ICASSP 2025\u20142025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India.","DOI":"10.1109\/ICASSP49660.2025.10888811"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s41562-024-02046-9","article-title":"Large language models surpass human experts in predicting neuroscience results","volume":"9","author":"Luo","year":"2025","journal-title":"Nat. Hum. Behav."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/s41746-024-01083-y","article-title":"Evaluating large language models as agents in the clinic","volume":"7","author":"Mehandru","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_57","unstructured":"Saab, K., Tu, T., Weng, W.H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., and Vedadi, E. (2024). Capabilities of gemini models in medicine. arXiv."},{"key":"ref_58","unstructured":"Yang, L., Xu, S., Sellergren, A., Kohlberger, T., Zhou, Y., Ktena, I., Kiraly, A., Ahmed, F., Hormozdiari, F., and Jaroensri, T. (2024). Advancing multimodal medical capabilities of Gemini. arXiv."},{"key":"ref_59","unstructured":"Google Research and Google DeepMind (2025, September 09). Advancing Medical AI with Med-Gemini. Available online: https:\/\/research.google\/blog\/advancing-medical-ai-with-med-gemini\/."},{"key":"ref_60","unstructured":"Huynh, M., Kline, A., Surabhi, S., Dunlap, K., Mutlu, O.C., Honarmand, M., Azizian, P., Washington, P., and Wall, D.P. (2024). Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e31830","DOI":"10.2196\/31830","article-title":"Identification of social engagement indicators associated with autism spectrum disorder using a game-based mobile app: Comparative study of gaze fixation and visual scanning methods","volume":"24","author":"Varma","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lakkapragada, A., Kline, A., Mutlu, O.C., Paskov, K., Chrisman, B., Stockham, N., Washington, P., and Wall, D.P. (2022). The classification of abnormal hand movement to aid in autism detection: Machine learning study. JMIR Biomed. Eng., 7.","DOI":"10.2196\/33771"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Farooq, M.S., Tehseen, R., Sabir, M., and Atal, Z. (2023). Detection of autism spectrum disorder (ASD) in children and adults using machine learning. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35910-1"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s40747-025-01790-3","article-title":"Explainable and secure framework for autism prediction using multimodal eye tracking and kinematic data","volume":"11","author":"Almadhor","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"ref_65","unstructured":"Kaplan, J., McCandlish, S., Henighan, T., Brown, T.B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020). Scaling laws for neural language models. arXiv."},{"key":"ref_66","unstructured":"Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D.d.L., Hendricks, L.A., Welbl, J., and Clark, A. (2022). Training compute-optimal large language models. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e2311878121","DOI":"10.1073\/pnas.2311878121","article-title":"Explaining neural scaling laws","volume":"121","author":"Bahri","year":"2024","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/687\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:31:45Z","timestamp":1761888705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":67,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["a18110687"],"URL":"https:\/\/doi.org\/10.3390\/a18110687","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]}}}