Sağlıkta Yapay Zeka Uygulamaları

Yıl 2024, , 98 - 105, 30.06.2024

Ebru Uğraş Tiryaki , Erhan Şimşek

https://doi.org/10.17827/aktd.1439689

Öz

Bazı ülkelerde aile hekimleri olarak da adlandırılan genel muayenehaneler, birinci basamak sağlık hizmetlerinin temel taşıdır. Ortalama yaşam süresinin ve buna bağlı olarak kronik hastalıkların sayısının artması, son dönemde pratisyen hekimlerin iş yükünü artırmış ve hastaya ayıracak zamanı azaltmıştır. Sağlık profesyonellerinin işlerini kolaylaştırmak için pratisyen hekimlerde Yapay zeka (AI) destekli sistemlerin uygulanması önemlidir. Yapay zeka destekli sistemlerin uygulanmasının sağlık profesyonellerine teşhis ve tedavide yardımcı olması beklenir. Yapay zeka, derin öğrenme ve makine öğrenimi de dahil olmak üzere bir dizi teknolojiyi kapsayan, insanın bilişsel yeteneklerinin makine simülasyonunu içerir. Yapay zeka şu anda tıpta çeşitli uygulamalarda kullanılıyor ve gelişmeye devam etmektedir ve tıptaki rolünün giderek daha belirgin hale gelmesi beklenmektedir. Yapay zeka destekli sensör sistemleri, fizyolojik parametreleri sürekli izleyerek kişiselleştirilmiş tıbbi tedavi oluşturabilir. Ancak yapay zekanın pratisyen hekimlerde kullanılması henüz başlangıç aşamasındadır. Yapay zeka, sağlık profesyonellerinin uzmanlıklarının yerine geçmek yerine, teşhisin doğruluğunu ve hızını artırmalarına yardımcı olan bir araçtır. Bu derlemede yapay zekanın genel muayenehanelerde (GP) uygulanmasına odaklanacağız.

Anahtar Kelimeler

Hastalık Yönetimi, Yapay Zeka, Aile Hekimliği, Birinci Basamak Sağlık Hizmeti

Destekleyen Kurum

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Proje Numarası

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Artificial Intelligence Applications in Health

Öz

General practices (GPs), called family physicians in certain countries, are the cornerstone of primary health care. The increase in average lifespan and, thereby, the number of chronic diseases has recently increased the workload of GPs and decreased the time spent on the patient. Implementations of Artificial intelligence (AI)-powered systems are essential in GPs to facilitate the jobs of health professionals. Implementing AI-driven systems is expected to help health professionals diagnose and treat. AI involves the machine simulation of human cognitive capabilities, encompassing a range of technologies, including deep learning and machine learning. AI is currently being used across various applications in medicine and continues to evolve, and its role in medicine is expected to become increasingly prominent. AI-enhance sensor systems can continuously monitor physiological parameters and generate personalized medicinal therapy. However, the employment of AI in GPs is still in the very early phase. AI is a tool to aid healthcare professionals in improving the accuracy and speed of diagnosis rather than a replacement for their expertise. This review will focus on applying artificial intelligence in general practices (GPs).

Anahtar Kelimeler

Artificial Intelligence, Family Medicine, Primary Care, Disease Management

Proje Numarası

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Kaynakça

• 1. Meskó, B. The Real Era of the Art of Medicine Begins with Artificial Intelligence. J Med Internet Res 2019;21(11):e16295.
• 2. Averbuch, T., Sullivan, K., Sauer, A., Mamas, M. A., Voors, A. A., Gale, C. P. et al. Applications of artificial intelligence and machine learning in heart failure. Eur Heart J Digit Health, 2022:3(2);311-22.
• 3. Kann, Benjamin H., Ahmed Hosny, Hugo JWL Aerts. Artificial intelligence for clinical oncology. Cancer Cell 2021;39(7):916-27.
• 4. Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J. et al. Toward understanding the impact of artificial intelligence on labor. Proc Natl Acad Sci U S A, 2019;116(14):6531-9.
• 5. Miller, D.D., Brown, E.W.Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med 2018;131(2):129-33.
• 6. Rogers, M.A., Aikawa E. Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery. Nat Rev Cardiol 2019;16(5):261-74.
• 7. Díaz, Ó, James AR D, Jesús G. Artificial intelligence: a novel approach for drug discovery. Trends Pharmacol Sci 2019;40(8):550-1.
• 8. Sørensen, N. L., Bemman, B., Jensen, M. B., Moeslund, T. B., Thomsen, J. L. Machine learning in general practice: scoping review of administrative task support and automation. BMC Prim Care. 2023;24(1):14.
• 9. Lin, S. Y., Mahoney, M. R., Sinsky, C. A. Ten ways artificial intelligence will transform primary care. J Gen Intern Med 2019;34(8):1626-30.
• 10. Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A.et al. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of clinical bioinformatics, 2015;5(1),1-16.
• 11. Hersh, W.R. Medical informatics: improving health care through information. JAMA 2002;288(16):1955-8.
• 12. Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R. et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1),29-43.
• 13. Sermesant, M., Delingette, H., Cochet, H., Jais, P., Ayache, N. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol.2021;18(8),600-9
• 14. Davenport, T. Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc J 2019;6(2),94-8.
• 15. Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J. et al. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J. 2018;69(2),120-35
• 16. Hobbs, F. R., Bankhead, C., Mukhtar, T., Stevens, S., Perera-Salazar, R., Holt, T. et al. Clinical workload in UK primary care: a retrospective analysis of 100 million consultations in England, 2007–14. Lancet, 2016;387(10035), 2323-30
• 17. Pedersen, K. M., Andersen, J. S., Søndergaard, J. General practice and primary health care in Denmark. J Am Board Fam Med. 2012;25(Suppl 1), S34-8.
• 18. Singh, H., Giardina, T. D., Meyer, A. N., Forjuoh, S. N., Reis, M. D., Thomas, E. J. Types and origins of diagnostic errors in primary care settings. JAMA Intern Med. 2013;173(6),418-25.
• 19. Van Such, M., Lohr, R., Beckman, T., Naessens, J. M. Extent of diagnostic agreement among medical referrals. J Eval Clin Pract.2017;23(4), 870-874.
• 20. Lambe, K. A., O'Reilly, G., Kelly, B. D., Curristan, S. Dual-process cognitive interventions to enhance diagnostic reasoning: a systematic review. BMJ Qual Saf. 2016;25(10):808-20
• 21. Police, R. L., Foster, T., Wong, K. S. Adoption and use of health information technology in physician practice organisations: systematic review. Inform Prim Care. 2010;18(4):245-58
• 22. Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8),1836-42.
• 23. Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A. Artificial intelligence–based breast cancer nodal metastasis detection: Insights into the black box for pathologists. Arch Pathol Lab Med. 2019;143(7),859-68.
• 24. Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P. et al.Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med, 2019;25(1), 65-9.
• 25. Willis, M., Duckworth, P., Coulter, A., Meyer, E. T., Osborne, M. Qualitative and quantitative approach to assess the potential for automating administrative tasks in general practice. BMJ Open, 2020;10(6):e032412
• 26. Harbishettar, V., Krishna, K. R., Srinivasa, P., Gowda, M. The enigma of doctor-patient relationship. Indian J Psychiatry. 2019;61(Suppl 4), S776-81.
• 27. Israni, S.T. Verghese, A. Humanizing Artificial Intelligence. JAMA 2019;321(1): 29-30.
• 28. Nurek, M., Kostopoulou, O., Delaney, B. C., Esmail, A. Reducing diagnostic errors in primary care. A systematic meta-review of computerized diagnostic decision support systems by the LINNEAUS collaboration on patient safety in primary care. Eur J Gen Pract. 2015;21(sup1):8-13.
• 29. Cubillas, J. J., Ramos, M. I., Feito, F. R.,Ureña, T. An improvement in the appointment scheduling in primary health care centers using data mining. J Med Syst. 2014;38(8):1;89.
• 30. Park, J., Kotzias, D., Kuo, P., Logan Iv, R. L., Merced, K., Singh, S. et al. Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions. J Am Med Inform Assoc. 2019;26(12):1493-504.
• 31. Schwartz, J. L., Tseng, E., Maruthur, N. M.,Rouhizadeh, M. Identification of prediabetes discussions in unstructured clinical documentation: validation of a natural language processing algorithm. JMIR Med Inform. 2022;10(2):e29803.
• 32. Singareddy S, Sn VP, Jaramillo AP, Yasir M, Iyer N, Hussein S, et al. Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus. 2023;15(9):e46066.
• 33. Yach, D., Hawkes, C., Gould, C. L., Hofman, K. J. The global burden of chronic diseases: overcoming impediments to prevention and control. JAMA, 2004;291(21):2616-22.
• 34. Schachner, T., Keller, R., v Wangenheim, F. (2020). Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. J Med Internet Res. 2020;22(9):e20701.
• 35. Kvedar, J. C., Fogel, A. L., Elenko, E., Zohar, D. Digital medicine's march on chronic disease. Nat Biotechnol. 2016;34(3):239-46.
• 36. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., Kitai, T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21): 2657-64.
• 37. Shen, J., Zhang, C. J., Jiang, B., Chen, J., Song, J., Liu, Z. et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inform. 2019;7(3):e10010.
• 38. Thylefors, B. A global initiative for the elimination of avoidable blindness. Community Eye Health. 1998;11(25):1-3.
• 39. Martínez-Sellés, M. Marina-Breysse, M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023;10(4):175
• 40. Jamthikar, A., Gupta, D., Johri, A. M., Mantella, L. E., Saba, L., Suri, J. S. A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study. Comput Biol Med. 2022;140:105102.
• 41. Quer G, Arnaout R, Henne M, Arnaout R. Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review. J Am Coll Cardiol. 2021;77(3): 300-13.
• 42. Than, M. P., Pickering, J. W., Sandoval, Y., Shah, A. S., Tsanas, A., Apple, F. S. et al. Machine learning to predict the likelihood of acute myocardial infarction. Circulation, 2019;140(11):899-909.
• 43. Quartieri, F., Marina-Breysse, M., Pollastrelli, A., Paini, I., Lizcano, C., Lillo-Castellano, J. M. Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study. Cardiovasc Digit Health J. 2022;3(5):201-11.
• 44. Oh, S. L., Ng, E. Y., San Tan, R., Acharya, U. R. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med. 2018;102:278-87.
• 45. Taggar, J. S., Coleman, T., Lewis, S., Heneghan, C., Jones, M. Accuracy of methods for detecting an irregular pulse and suspected atrial fibrillation: a systematic review and meta-analysis. Eur J Prev Cardiol. 2016;23(12):1330-8.
• 46. Ding, H., Chen, S. H., Edwards, I., Jayasena, R., Doecke, J., Layland, J. et al. Effects of different telemonitoring strategies on chronic heart failure care: systematic review and subgroup meta-analysis. J Med Internet Res. 2020;22(11):e20032.
• 47. Faragli, A., Abawi, D., Quinn, C., Cvetkovic, M., Schlabs, T., Tahirovic, E. et al. The role of non-invasive devices for the telemonitoring of heart failure patients. Heart Fail Rev.2021;26(5):1063-80.
• 48. Visco, V., Finelli, R., Pascale, A. V., Giannotti, R., Fabbricatore, D., Ragosa, N. et al. Larger blood pressure reduction by fixed-dose compared to free dose combination therapy of ace inhibitor and calcium antagonist in hypertensive patients. Transl Med UniSa. 2017;16:17-23.
• 49. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet. 2017;389(10064):37-55. Erratum in: Lancet. 2020;396(10255):886.
• 50. Whitworth, J.A. Chalmers, J. World health organisation-international society of hypertension (WHO/ISH) hypertension guidelines. Clin Exp Hypertens 2004;26(7-8): 747-52.
• 51. Dzau, V.J. Balatbat, C.A. Future of Hypertension. Hypertension. 2019;74(3):450-7.
• 52. Campbell, N. R., Chockalingam, A., Fodor, J. G.,McKay, D. W. Accurate, reproducible measurement of blood pressure. CMAJ. 1990;143(1):19.
• 53. Quan, X., Liu, J., Roxlo, T., Siddharth, S., Leong, W., Muir, A. Advances in non-invasive blood pressure monitoring. Sensors (Basel), 2021;21(13):4273.
• 54. Visco, V., Izzo, C., Mancusi, C., Rispoli, A., Tedeschi, M., Virtuoso, N. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. J Cardiovasc Dev Dis. 2023;10(2):74.
• 55. Koren, G., Nordon, G., Radinsky, K., Shalev, V. Machine learning of big data in gaining insight into successful treatment of hypertension. Pharmacol Res Perspect. 2018;6(3):e00396.
• 56. Chaikijurajai T, Laffin LJ, Tang WHW. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. Am J Hypertens 2020;33(11):967-74.
• 57. Hermida, R. C., Smolensky, M. H., Ayala, D. E., Portaluppi, F. Ambulatory Blood Pressure Monitoring (ABPM) as the reference standard for diagnosis of hypertension and assessment of vascular risk in adults. Chronobiol Int. 2015;32(10): 1329-42.
• 58. Pierdomenico, S.D.Cuccurullo, F. Prognostic value of white-coat and masked hypertension diagnosed by ambulatory monitoring in initially untreated subjects: an updated meta analysis. Am J Hypertens 2011;24(1):52-8.
• 59. Asayama, K., Thijs, L., Li, Y., Gu, Y. M., Hara, A., Liu, Y. P. et al. Setting thresholds to varying blood pressure monitoring intervals differentially affects risk estimates associated with white-coat and masked hypertension in the population. Hypertension, 2014;64(5):935-42.
• 60. Bryan, C., Boren, S.A. The use and effectiveness of electronic clinical decision support tools in the ambulatory/primary care setting: a systematic review of the literature. Inform Prim Care 2008;16(2):79-91.
• 61. Asan, O., Bayrak, A. E., Choudhury, A. Artificial intelligence and human trust in healthcare: focus on clinicians. J Med Internet Res. 2020;22(6): e15154.
• 62. Patel, B., Makaryus, A.N. Cardiac implantable electronic devices and cybersecurity. Expert Rev Med Devices 2021;18(sup1):69-77.
• 63. Miller, D.D.Machine Intelligence in Cardiovascular Medicine. Cardiol Rev. 2020;28 (2):53-64.