Advancements in Data Analysis and Chemistry for Health, Medicine, and Dentistry: A Systematic Review

Abstract

Combining analytical chemistry with sophisticated data processing technologies has now become the main asset of contemporary precision medicine and dentistry. In the current review, the symbiotic connection between high-sensitivity chemical instrumentation and computational intelligence will be discussed covering the years of 2022-2025. We assess how the fourth industrial revolution can bring about change in healthcare by transforming biochemical signals into actionable clinical data by digital means. The most relevant methods are chemometric tools, such as Principal Component Analysis and Partial Least Squares, and machine learning models that can facilitate the analysis of the more complicated dataset of spectroscopy and chromatographic results. Key results show that AI-enriched sensors and real-time monitoring systems lead to a crucial increase in the accuracy of diagnosing oral pathologies and chronic systemic diseases. Nevertheless, there are still crucial gaps in terms of the absence of standardized guidelines, considerable data inconsistency, and high prices of advanced analytical systems. The future perspective focuses on the emergence of the green analytical chemistry and the introduction of the Internet of Biochemical Things that will provide opportunities to get personalized healthcare.

Keywords

References

1. 1. Wang, C., et al., Artificial intelligence enhanced sensors-enabling technologies to next-generation healthcare and biomedical platform. Bioelectronic Medicine, 2023. 9(1): p. 17.
2. 2. Verma, V. and D. Kumar, Artificial intelligence and machine learning in drug discovery: From lead discovery to clinical validation (2020–2025). Letters in Drug Design & Discovery, 2026: p. 100341.
3. 3. Singh, J., S. Singh, and A. Verma, Artificial intelligence in use of ZrO2 material in biomedical science.
4. Journal of Electrochemical Science and Engineering, 2023. 13(1): p. 83-97.
5. 4. Rohman, A. and R.T.C. Tandelilin, Molecular spectroscopy as an analytical tool for the detection of oral cancer, in Oral Diagnostics Tools and Techniques: A physicist’s approach. 2023, IOP
6. Publishing Bristol, UK. p. 1-1.
7. 5. Feher, B., C. Tussie, and W.V. Giannobile, Applied artificial intelligence in dentistry: emerging data
8. modalities and modeling approaches. Frontiers in artificial intelligence, 2024. 7: p. 1427517.
9. 6. Gokdeniz, S.T., A. Buyuksungur, and M.E. Kolsuz, Artificial Intelligence in Dentistry, in Human
10. Teeth-From Function to Esthetics. 2023, IntechOpen.
11. 7. Dai, J., et al., Artificial intelligence for medicine 2025: Navigating the endless frontier. The
12. Innovation Medicine, 2025. 3(1): p. 100120-1.
13. 8. Reyes, L.T., et al., Scope and challenges of machine learning-based diagnosis and prognosis in
14. clinical dentistry: A literature review. Journal of clinical and translational research, 2021. 7(4): p.
15. 523.
16. 9. Arsiwala-Scheppach, L.T., et al., Machine learning in dentistry: a scoping review. Journal of Clinical
17. Medicine, 2023. 12(3): p. 937.
18. 10. Page, M.J., et al., The PRISMA 2020 statement: an updated guideline for reporting systematic
19. reviews. bmj, 2021. 372.
20. 11. Kreve, S. and A.C.d. Reis, Systematic Review of In Vitro Studies in Dentistry: How to Conduct It?
21. Journal of Advanced Oral Research, 2025. 16(1): p. 17-24.
22. 12. Pereira-Cenci, T., et al., Citation metrics and quality of the top cited systematic reviews in dentistry:
23. a meta-research. 2024.
24. 13. Sewell, K.A., J. Schellinger, and J.E. Bloss, Effect of PRISMA 2009 on reporting quality in systematic
25. reviews and meta-analyses in high-impact dental medicine journals between 1993–2018. PLoS
26. One, 2023. 18(12): p. e0295864.
27. 14. Kallianta, M., et al., Applications of mass spectrometry in dentistry. Biomedicines, 2023. 11(2): p.
28. 286.
29. 15. De Angelis, F., et al., Meta-analytical analysis on components released from resin-based dental
30. materials. Clinical oral investigations, 2022. 26(10): p. 6015-6041.
31. 16. Chengappa, K., et al., Heavy metal content of over-the-counter toothpastes—a systematic review
32. of in vitro studies. Frontiers in Dental Medicine, 2025. 6: p. 1543972.
33. 17. Rodríguez-Martínez, J., M.-J. Sánchez-Martín, and M. Valiente, Efficient controlled release of
34. cannabinoids loaded in γ-CD-MOFs and DPPC liposomes as novel delivery systems in oral health.
35. Microchimica Acta, 2023. 190(4): p. 125.
36. 18. Solow, R., Systematic review versus structured critical analysis. CRANIO®, 2021.
37. 19. Malarvannan, M., et al., Assessment of computational approaches in the prediction of
38. spectrogram and chromatogram behaviours of analytes in pharmaceutical analysis: assessment
39. review. Future Journal of Pharmaceutical Sciences, 2023. 9(1): p. 86.
40. 20. Saha, P., et al., A comprehensive review on the applications of chemometrics in analytical
41. chemistry. Journal of Applied Pharmaceutical Research, 2025. 13(3): p. 1-16.
42. 21. Elragal, R., A. Elragal, and A. Habibipour, Healthcare analytics—A literature review and proposed
43. research agenda. Frontiers in big Data, 2023. 6: p. 1277976.
44. 22. Thomas, J.C., K. Shin, and X.J. Xie, Principal Component Analysis in Dental Research. International Journal of Oral & Maxillofacial Implants, 2025. 40(1).
45. 23. Rahman, M.A.A., et al., Novel analytical method based on chemometric models applied to UV–Vis spectrophotometric data for simultaneous determination of Etoricoxib and Paracetamol in presence of Paracetamol impurities. BMC chemistry, 2023. 17(1): p. 176.
46. 24. Rahman, J., et al., Distance plus attention for binding affinity prediction. Journal of Cheminformatics, 2024. 16(1): p. 52.
47. 25. Meto, A. and A. Meto, Evaluation of dental materials and oral disease-related proteins in dentistry: Efficacy of electrophoresis as a valuable tool, in Electrophoresis-Recent Advances, New Perspectives and Applications. 2023, IntechOpen.
48. 26. Nelson, A., Electrochemical Sensing of Environmental and Biological Samples. 2023.
49. 27. Patil, S., et al., Artificial intelligence in the diagnosis of oral diseases: applications and pitfalls.
50. Diagnostics, 2022. 12(5): p. 1029.
51. 28. Mahbub, P., et al., Scalable production of hydroxyl radicals (. OH) via homogeneous photolysis of
52. hydrogen peroxide using a continuous-flow photoreactor. Chemical Engineering Journal, 2022.
53. 427: p. 131762.
54. 29. Florea, L. and D. Diamond, Sensors and “The internet of biochemical things”. Frontiers in Sensors,
55. 2026 ;2 (2):18
56. 2022. 3: p. 1010212.

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