What is precision medicine?
Precision medicine, also known as personalized medicine or systems medicine, is a data-driven approach to medical care that considers genetic, lifestyle, and environmental factors to guide treatment decisions [1]. It is hypothesis-generatingrather thanhypothesis-driven, the treatment plan is shaped by patterns that emerge from the data analysis, rather than by a predefined assumption. Essentially, we want to give the “right treatment to the right patient at the right time.” This strategy allows medical professionals to stratify people into groups based on which prevention strategies and treatments will work better for them. Traditional medicine follows a more one-size-fits-all approach, in which a treatment is given to everyone with a certain condition, but it works better for some than for others.
Precision medicine relies on a combination of detailed clinical notes, biomarkers, imaging, lab tests, and genomic sequencing to create certain profiles, known as phenotypes. Deep phenotyping refers to going beyond the superficial level of simply identifying the disease to understand “Exactly how does this disease uniquely present in this person, at multiple biological and clinical levels?” These multidimensional data are then analyzed across large patient populations to uncover subtle patterns and previously unrecognized disease subtypes. By integrating these diverse data layers, clinicians can more accurately stratify patients, predict disease progression, and tailor therapies to maximize efficacy while minimizing adverse effects. This approach is already well established in oncology, immunotherapy, pharmacogenetics, and the management of rare diseases.
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How can precision medicine be applied in dentistry and, more specifically, in orofacial pain?
Dentistry, conventionally, has been very treatment-driven – if a tooth has a cavity, we fill it. If a pulp is necrotic, we treat it endodontically. If a patient has gingival pocketing with bleeding, we scale their teeth. These treatments work – they get patients out of pain, restore function and health, and can be maintained with routine care. While these approaches are effective at resolving active disease, they may not fully account for individual biological susceptibility or behavioral and environmental risk factors that influence disease onset and recurrence. Patients are generally educated about the harms of a sugary diet, but we could do more to understand specific salivary biomarkers that may predispose a patient to caries, or, for that matter, oral cancer [2]. We now know that there are genetic, metabolic, and lifestyle predispositions for periodontal disease; by integrating multidimensional patient data, predictive and preventive care models could be developed that are more precisely tailored to individual risk profiles and long-term outcomes.
These limitations become even more pronounced in the context of orofacial pain (OFP), where diagnostic heterogeneity and multifactorial influences complicate clinical decision-making. Additionally, many dentists are not well-trained in diagnosis and management of OFP conditions, which may present with a wide range of symptoms and are highly influenced by psychological and lifestyle factors. In the conventional framework, both misdiagnoses and missed diagnoses are common in non-specialty practices. The recent International Classification of Orofacial Pain (ICOP) lists around 200 distinct conditions, and these do not include non-painful conditions, oral motor conditions, or sleep-related disorders that may occur [3, 4]. A standardized algorithm, as created by the ICOP, is an excellent first step but can be overwhelming to follow manually; the full document is around 86 pages. However, with a data-driven system that can evaluate variables both individually and in relation to each other and create specific phenotypes for each condition, diagnosis becomes more streamlined and standardized. Incorporating machine learning–based risk modeling and pattern recognition can further enhance this approach by identifying meaningful relationships among clinical, biological, and psychosocial variables, allowing for more accurate diagnosis, prediction of outcomes, and individualized treatment planning. In fact, as data is input from many patients over multiple visits, we can even begin to predict outcomes for patients based on their particular phenotypes and treat or prevent accordingly.
Figure 1: Precision Biopsychosocial Model of Orofacial Pain in Dentistry
Has this been demonstrated in a model?
At USC’s specialty OFP clinic, Drs. Vistoso and Clark et al. have pioneered an artificial intelligence–machine learning (AI–ML) driven diagnostic model integrated within a structured electronic health record (EHR) platform [4,5]. Unlike traditional narrative-based documentation, this system uses standardized data fields, reducing the risk of missing items during an examination and enhancing data uniformity across clinics and faculties. As patient data is entered, the AI–ML algorithm analyzes the variables in real time and compares them against previously identified phenotypic clusters to generate possible differential diagnoses. The initial model was developed using a dataset of 451 patients and 141 curated clinical variables; as additional cases are incorporated and new variables are introduced, the system continues to learn and refine its predictive performance [5]. Beyond diagnostic support, the platform facilitates multidisciplinary collaboration by creating a shared, structured data environment accessible to oral medicine specialists, pain clinicians, pathologists, and other healthcare providers. Importantly, integrating longitudinal data may enable risk stratification and early identification of high-risk patterns, including the potential prediction of malignant transformation in susceptible lesions. Such systems represent a foundational step toward scalable precision dentistry, where data-driven phenotyping informs both diagnosis and proactive intervention.
Conclusion
Precision medicine represents a natural evolution of healthcare in an increasingly data-driven world. While traditional, experience-based practice has served medicine and dentistry well, it is no longer sufficient to rely solely on generalized treatment models when we have the tools to better understand individual variation. By incorporating standardized and structured data into everyday clinical care, we can improve not only diagnostic accuracy, but also prevention strategies, risk assessment, and long-term disease management. Care becomes more proactive rather than reactive. Standardization also creates a shared clinical language. When patient data are organized consistently, collaboration across specialties becomes more seamless, whether between general dentists and oral medicine specialists or between dental and medical providers. This interconnected approach is particularly valuable in complex fields such as orofacial pain, where biological, psychological, and social factors intersect.
The transition away from narrative documentation toward structured, data-informed systems will not be without challenges. Concerns regarding patient privacy, data security, clinician training, and workflow adaptation must be addressed thoughtfully and ethically. However, these challenges are not insurmountable. With appropriate safeguards and responsible implementation, the potential benefits are substantial. In fact, we may reduce overall healthcare costs by coordinating care and developing tailored prevention strategies based on individual phenotypes.
Ultimately, precision medicine shifts the focus from treating diseases in isolation to understanding patients as individuals with unique risk profiles and needs. For patients with rare conditions, atypical presentations, or chronic and multifactorial pain, this approach offers the possibility of earlier diagnosis, more targeted interventions, and improved long-term outcomes. As dentistry continues to evolve, embracing data-driven, patient-centered care may not simply be an innovation; it may become a necessity.
Are you interested in a variety of issues focused on orofacial pain, medicine and sleep disorders? Consider enrolling in the Herman Ostrow School of Dentistry of USC’s online, competency-based certificate or master’s program in Orofacial Pain and Oral Medicine.
References
- Cleveland Clinic. (2023, September 28). Precision medicine. Cleveland Clinic. https://my.clevelandclinic.org/health/articles/precision-medicine
- Kaur J, Jacobs R, Huang Y, Salvo N, Politis C. Salivary biomarkers for oral cancer and pre-cancer screening: a review. Clin Oral Investig. 2018 Mar;22(2):633-640. doi: 10.1007/s00784-018-2337-x. Epub 2018 Jan 17. PMID: 29344805.
- Shakeri H, Vueghs C, Benoliel R, May A, Conti P, Renton T, Baad-Hansen L, Van der Cruyssen F. Development and validation of the International Classification for Orofacial Pain Algorithm. Pain. 2026 Jan 1;167(1):e1-e7. doi: 10.1097/j.pain.0000000000003783. Epub 2025 Aug 13. PMID: 40829056; PMCID: PMC12709632.
- Paulina Vistoso Monreal A, Veas N, Clark G. An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain. Jpn Dent Sci Rev. 2021 Nov;57:242-249. doi: 10.1016/j.jdsr.2021.11.001. Epub 2021 Nov 20. PMID: 34849180; PMCID: PMC8608603.
- Nocera L, Vistoso A, Yoshida Y, Abe Y, Nwoji C, Clark GT. Building an Automated Orofacial Pain, Headache and Temporomandibular Disorder Diagnosis System. AMIA Annu Symp Proc. 2021 Jan 25;2020:943-952. PMID: 33936470; PMCID: PMC8075456.
