Exploration of Medicine

Table 1. Illustrative multimodal signals and analytical roles for integrative AI across the triangle (non-exhaustive).

Domain	Representative data streams	AI applications/Analytical goals
Facial trauma	CT/CBCT volumes; segmentation masks; 3D meshes; operative notes; fix-ation metadata; perioperative labora-tory results	Image intelligence for fracture detection and morphology characterisation; outcome and complication risk estimation; simulation of healing trajectories
Oral health	Intraoral photographs; radiographs (bitewing, panoramic); periodontal charts; histopathology WSI (whole slide imaging); microbiome profiles	Automated disease detection; progression and mediation modelling; causal inference linking oral inflammation with systemic and surgical outcomes
Systemic	EHR summaries; medication history; vital signs; laboratory data (HbA1c, CRP, lipid panels); DEXA; ECG/echocardiography; wearable-derived signals	Multimodal data fusion for systemic risk estimation; longitudinal outcome prediction; identification of feedback effects influencing trauma recovery and oral health

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AI: artificial intelligence; CBCT: cone beam computed tomography; CT: computed tomography; CRP: C-reactive protein; ECG: electrocardiography.

Declarations

Author contributions

TDP: Conceptualization, Investigation, Methodology, Visualization, Writing—original draft, Writing—review & editing. The author read and approved the submitted version.

Conflicts of interest

Tuan D. Pham, who is the Editorial Board Member of Exploration of Medicine, had no involvement in the decision-making or the review process of this manuscript. There are no other conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

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Not applicable.

Availability of data and materials

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Funding

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References

Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell. 2024;6:1278529. [DOI] [PubMed] [PMC]

Long J, Yang L, Dou J, Zhang L, Tan X. Artificial intelligence in dentistry: a bibliometric analysis. Br Dent J. 2025;[Epub ahead of print]. [DOI] [PubMed]

Abdul NS, Shivakumar GC, Sangappa SB, Di Blasio M, Crimi S, Cicciù M, et al. Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis. BMC Oral Health. 2024;24:122. [DOI] [PubMed] [PMC]

Yamazaki K, Kamada N. Exploring the oral-gut linkage: Interrelationship between oral and systemic diseases. Mucosal Immunol. 2024;17:147–53. [DOI] [PubMed] [PMC]

Natarajan P, Madanian S, Marshall S. Investigating the link between oral health conditions and systemic diseases: A cross-sectional analysis. Sci Rep. 2025;15:10476. [DOI] [PubMed] [PMC]

Pham TD, Holmes S, Chatzopoulou D, Coulthard P. Artificial Intelligence in Facial Trauma, Oral Diseases, and Systemic Health. Cham: Springer Cham; 2026. [DOI]

Lieber T, Gosselt HR, Kools PC, Kruijssen OC, Van Lierop SNC, Härmark L, et al. Natural language processing for automated triage and prioritization of individual case safety reports for case-by-case assessment. Front Drug Saf Regul. 2023;3:1120135. [DOI] [PubMed] [PMC]

Raith S, Pankert T, Jaganathan S, Pankert K, Lee H, Peters F, et al. Segmenting beyond the imaging data: creation of anatomically valid edentulous mandibular geometries for surgical planning using artificial intelligence. Clin Oral Investig. 2025;29:501. [DOI] [PubMed] [PMC]

Franceschini C, Ahmadi M, Zhang X, Wu K, Lin M, Weston R, et al. Revolutionizing spine surgery with emerging AI-FEA integration. J Robot Surg. 2025;19:615. [DOI] [PubMed] [PMC]

10.

Alasiri M. Potential applicability of virtual reality in implant dentistry: a narrative review. Front Dent Med. 2024;5:1491268. [DOI] [PubMed]

11.

Arjmandnia F, Alimohammadi E. The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review. Patient Saf Surg. 2024;18:11. [DOI] [PubMed] [PMC]

12.

McLean KA, Sgrò A, Brown LR, Buijs LF, Mountain KE, Shaw CA, et al. Multimodal machine learning to predict surgical site infection with healthcare workload impact assessment. NPJ Digit Med. 2025;8:121. [DOI] [PubMed]

13.

Aseri AA. Exploring the Role of Artificial Intelligence in Dental Implantology: A Scholarly Review. J Pharm Bioallied Sci. 2025;17:S102–4. [DOI] [PubMed]

14.

Vázquez-Sebrango G, Anitua E, Macía I, Arganda-Carreras I. The role of artificial intelligence in implant dentistry: a systematic review. Int J Oral Maxillofac Surg. 2025;54:1098–122. [DOI] [PubMed]

15.

Jindanil T, Marinho-Vieira LE, de-Azevedo-Vaz SL, Jacobs R. A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal. Dentomaxillofacial Radiol. 2023;52:20230321. [DOI] [PubMed] [PMC]

16.

Yang F, Wu X, Zhang Y, Lang X, Yang Y, Ma L, et al. A deep learning based automated maxillary sinus segmentation and bone grafts analysis in CBCT images. NPJ Digit Med. 2025;9:90. [DOI] [PubMed] [PMC]

17.

Issa J, Dyszkiewicz Konwinska M, Kazimierczak N, Olszewski R. Assessing the accuracy of artificial intelligence in mandibular canal segmentation compared to semi-automatic segmentation on cone-beam computed tomography images. Pol J Radiol. 2025;90:172–9. [DOI] [PubMed]

18.

Ju J, Qu Z, Qing H, Ding Y, Peng L. Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis. BMC Musculoskelet Disord. 2025;26:682. [DOI] [PubMed] [PMC]

19.

Menon SS, Jacob SA, Eldho Paul A, Kurumathur Vasudevan A, Balakrishnan B, Rajan Peter M, et al. Use of Artificial Intelligence in Dental Implant Navigation Systems: A Scoping Review. Cureus. 2026;18:e100776. [DOI] [PubMed] [PMC]

20.

Khaohoen A, Yoda N, Rungsiyakull P, Rungsiyakull C, Taichi T. Can artificial intelligence optimize treatment planning and outcome prediction in fixed tooth- and implant-supported prosthodontics? A scoping review. BMC Oral Health. 2025;26:104. [DOI] [PubMed] [PMC]

21.

Alfaraj A, Limones Á, Ahmad S, Aljubairah F, Albalaw S, Albesher M, et al. Harnessing AI in prosthodontics and implant dentistry: An umbrella review of systematic evidence. J Prosthodont. 2026;35:127–42. [DOI] [PubMed] [PMC]

22.

Prasetia R, Liu F, Hananto JE, Purwana SZB, Kholinne E, Dilogo IH. Integrating generative artificial intelligence into orthopaedics: A review of opportunities, challenges and future directions. J Orthop Surg. 2026;34:10225536261424034. [DOI] [PubMed]

23.

Rana M, Sakkas A, Zimmermann M, Kostyuk M, Schwarz G. Artificial Intelligence in Oral and Maxillofacial Surgery: Integrating Clinical Innovation and Workflow Optimization. J Clin Med. 2026;15:427. [DOI] [PubMed] [PMC]

24.

Prasher P, Tuteja A, Amin F, Sopaj R, Tuteja D, Eddin HS. Advancing precision: The role of digital workflows in modern implant dentistry. Bioinformation. 2025;21:2490–5. [DOI]

25.

Neji G, Gasparro R, Tlili M, Dhahri A, Khanfir F, Sammartino G, et al. AI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes. J Clin Med. 2025;15:228. [DOI] [PubMed] [PMC]

26.

Mun SB, Yoo SR, Kim YJ, Kim K, Kim BC, Kim KG. AI-driven prediction of dental implant numbers to be placed for patient-specific treatment planning. Int Dent J. 2025;75:103896. [DOI] [PubMed]

27.

Szabó V, Szabó BT, Orhan K, Veres DS, Manulis D, Ezhov M, et al. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs. J Dent. 2024;147:105105. [DOI] [PubMed]

28.

Alqutaibi AY, Al-Zaghruri AS. ARTIFICIAL INTELLIGENCE DEMONSTRATES POTENTIAL IN DETECTING CARIES ON BITEWING RADIOGRAPHS, BUT FURTHER HIGH-QUALITY STUDIES ARE REQUIRED. J Evid-Based Dent Pract. 2025;25:102087. [DOI] [PubMed]

29.

Mirfendereski P, Li GY, Pearson AT, Kerr AR. Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review. Front Oral Health. 2025;6:1569567. [DOI] [PubMed] [PMC]

30.

Hassanein FEA, Ahmed Y, Maher S, Barbary AE, Abou-Bakr A. Prompt-dependent performance of multimodal AI model in oral diagnosis: a comprehensive analysis of accuracy, narrative quality, calibration, and latency versus human experts. Sci Rep. 2025;15:37932. [DOI] [PubMed] [PMC]

31.

Patel JS, Kumar K, Zai A, Shin D, Willis L, Thyvalikakath TP. Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records. Diagnostics. 2023;13:1028. [DOI] [PubMed] [PMC]

32.

Rebeiz T, Lawand G, Martin W, Gonzaga L, Revilla-León M, Khalaf S, et al. Development of an artificial intelligence model for assisting periodontal therapy decision-making: A retrospective longitudinal cohort study. J Dent. 2025;159:105780. [DOI] [PubMed]

33.

Song B, Liang R. Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo. Biosens Bioelectron. 2025;271:116982. [DOI] [PubMed] [PMC]

34.

Kapila YL. Oral health’s inextricable connection to systemic health: Special populations bring to bear multimodal relationships and factors connecting periodontal disease to systemic diseases and conditions. Periodontol 2000. 2021;87:11–6. [DOI] [PubMed] [PMC]

35.

Isola G, Polizzi A, Serra S, Boato M, Sculean A. Relationship between periodontitis and systemic diseases: A bibliometric and visual study. Periodontol 2000. 2025;98:228–40. [DOI] [PubMed] [PMC]

36.

Liu YCG, Lan SJ, Hirano H, Lin LM, Hori K, Lin CS, et al. Update and review of the gerodontology prospective for 2020’s: Linking the interactions of oral (hypo)-functions to health vs. systemic diseases. J Dent Sci. 2021;16:757–73. [DOI] [PubMed] [PMC]

37.

Mosaddad SA, Mahootchi P, Safari S, Rahimi H, Aghili SS. Interactions between systemic diseases and oral microbiota shifts in the aging community: A narrative review. J Basic Microbiol. 2023;63:831–54. [DOI] [PubMed]

38.

Lumelsky N. Oral-systemic immune axis: Crosstalk controlling health and disease. Front Dent Med. 2023;3:1106456. [DOI]

39.

Foroughi M, Torabinejad M, Angelov N, Ojcius DM, Parang K, Ravnan M, et al. Bridging oral and systemic health: exploring pathogenesis, biomarkers, and diagnostic innovations in periodontal disease. Infection. 2025;53:2277–302. [DOI] [PubMed] [PMC]

40.

Bida FC, Curca FR, Lupusoru R, Virvescu DI, Scurtu M, Rotundu G, et al. The Systemic Link Between Oral Health and Cardiovascular Disease: Contemporary Evidence, Mechanisms, and Risk Factor Implications. Diseases. 2025;13:354. [DOI] [PubMed] [PMC]

41.

Naka O, Chatzidou P, Sarafidou K, Anastassiadou V. Translating inflammaging: The bidirectional relationship between oral and systemic health in geriatric prosthodontics. Arch Gerontol Geriatr Plus. 2025;2:100192. [DOI]

42.

Martínez-García M, Hernández-Lemus E. Periodontal Inflammation and Systemic Diseases: An Overview. Front Physiol. 2021;12:709438. [DOI]

43.

Pham TD. Stomatognathic Diseases Reveal Bidirectional Link Between Diabetes Mellitus and Coronary Artery Calcium: A Cross‐Sectional Study Using Multi‐Way Array Analysis. Health Sci Rep. 2025;8:e71280. [DOI] [PubMed] [PMC]

44.

Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28:1773–84. [DOI] [PubMed]

45.

Simon BD, Ozyoruk KB, Gelikman DG, Harmon SA, Türkbey B. The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review. Diagn Interv Radiol. 2024;31:303–12. [DOI] [PubMed] [PMC]

46.

Teoh JR, Dong J, Zuo X, Lai KW, Hasikin K, Wu X. Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications. PeerJ Comput Sci. 2024;10:e2298. [DOI] [PubMed] [PMC]

47.

Kang W, Zhang X, Zhang J, Chen X, Huang H, He B, et al. Multimodal data generative fusion method for complex system health condition estimation. Sci Rep. 2025;15:20026. [DOI] [PubMed] [PMC]

48.

Chen X, Xie H, Tao X, Wang FL, Leng M, Lei B. Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics. Artif Intell Rev. 2024;57:e57. [DOI]

49.

Zhao D, Homayounfar M, Zhen Z, Wu MZ, Yu SY, Yiu KH, et al. A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions. Diagnostics. 2022;12:3192. [DOI] [PubMed] [PMC]

50.

Zhang J, Feng S, Xue J, Zhou C, Fu S, Liu P, et al. AI-driven multimodal fusion of tongue images and clinical indicators for identifying MAFLD patients at risk of coronary artery disease: An exploratory study. iLIVER. 2025;4:100181. [DOI] [PubMed] [PMC]

51.

Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A Comprehensive Survey on Graph Neural Networks. IEEE Trans Neural Netw Learn Syst. 2021;32:4–24. [DOI] [PubMed]

52.

Büttner M, Leser U, Schneider L, Schwendicke F. Natural Language Processing: Chances and Challenges in Dentistry. J Dent. 2024;141:104796. [DOI]

53.

Chuang YS, Lee CT, Lin GH, Brandon R, Jiang X, Walji MF, et al. Cross-institutional dental electronic health record entity extraction via generative artificial intelligence and synthetic notes. JAMIA Open. 2025;8:ooaf061. [DOI] [PubMed]

54.

Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119. [DOI] [PubMed]

55.

Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, et al. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology. 2023;140:85–101. [DOI] [PubMed] [PMC]

56.

Fu D, Shu X, Zhou G, Ji M, Liao G, Zou L. Connection between oral health and chronic diseases. MedComm. 2025;6:e70052. [DOI] [PubMed] [PMC]

57.

Liu S, Li X, Jiang S, Liu D, Wang J. A Review of Advances in Multimodal Treatment Strategies for Chronic Disorders of Consciousness Following Severe Traumatic Brain Injury. Int J Gen Med. 2025;18:771–86. [DOI] [PubMed] [PMC]

58.

Danish S, Sadeghi-Niaraki A, Khan SU, Dang LM, Tightiz L, Moon H. A comprehensive survey of Vision—Language Models: Pretrained models, fine-tuning, prompt engineering, adapters, and benchmark datasets. Inf Fusion. 2026;126:103623. [DOI]

59.

Corso G, Stark H, Jegelka S, Jaakkola T, Barzilay R. Graph neural networks. Nat Rev Methods Primers. 2024;4:e4. [DOI]

60.

Hangloo S, Arora B. Multimodal fusion techniques: Review, data representation, information fusion, and application areas. Neurocomputing. 2025;649:130827. [DOI]

61.

Pham TD. A deep learning vision—language model for diagnosing pediatric dental diseases. Intell-Based Med. 2026;13:100364. [DOI]

62.

Yadalam PK, Natarajan PM, Mosaddad SA, Heboyan A. Graph neural networks-based prediction of drug gene association of P2X receptors in periodontal pain. J Oral Biol Craniofacial Res. 2024;14:335–8. [DOI] [PubMed] [PMC]

63.

Liu J, Hao J, Lin H, Pan W, Yang J, Feng Y, et al. Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. Patterns. 2023;4:100825. [DOI] [PubMed] [PMC]

64.

Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M. Federated learning: Overview, strategies, applications, tools and future directions. Heliyon. 2024;10:e38137. [DOI] [PubMed] [PMC]

65.

Pham T. Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use. R Soc Open Sci. 2025;12:241873. [DOI] [PubMed] [PMC]

66.

Sun K, Wang X, Miao X, Zhao Q. A review of AI edge devices and lightweight CNN and LLM deployment. Neurocomputing. 2025;614:128791. [DOI]

67.

Dangi RR, Sharma A, Vageriya V. Transforming Healthcare in Low‐Resource Settings With Artificial Intelligence: Recent Developments and Outcomes. Public Health Nurs. 2024;42:1017–30. [DOI] [PubMed]