The role of artificial neural networks and clinical decision support systems in healthcare information systems

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The integration of artificial neural networks (ANNs) into clinical decision support systems based on health information systems represents a transformative shift in healthcare technologies that improves clinical decision making using advanced machine learning techniques. This evolution has arisen in response to the growing complexity and volume of medical data, which requires more sophisticated decision support tools that can provide personalized recommendations and improve patient outcomes. Neural networks, characterized by their ability to learn from large data sets, have played a critical role in developing predictive models that identify patient risks and suggest solutions, optimizing clinical workflows and improving the quality of care. The use of ANNs has sparked debates about the effectiveness, usability, and ethical implications of artificial intelligence (AI)-based decisions in healthcare. Research has shown significant improvements in predictive accuracy compared to traditional rule-based systems, but challenges in their implementation, including data quality, algorithmic bias, and the need for transparency in AI decision-making processes remain. The shift from traditional decision-making approaches to neural network-based systems is intensifying the debate around trust and explainability in healthcare technologies. While ANNs offer promising advances in medical decision-making, their black-box nature raises concerns about the reliability and transparency of the recommendations they generate among healthcare providers. Addressing these issues is essential to ensuring the integration of ANNs into healthcare information systems, ultimately aiming to provide equitable and effective patient care. As the field continues to evolve, ongoing and emerging research is critical to improving, mitigating potential biases, and enhancing the functionality of ANN-based clinical decision support systems. The convergence of AI and healthcare heralds a new era that has the potential to revolutionize clinical practice, but also requires careful consideration of the ethical implications and adherence to fundamental principles of patient care.

About the authors

I. G. Trukhanova

Samara State Medical University

Email: a.d.gureev@samsmu.ru
ORCID iD: 0000-0002-2191-1087
Russian Federation, Samara

A. D. Gureev

Samara State Medical University

Author for correspondence.
Email: a.d.gureev@samsmu.ru
ORCID iD: 0000-0001-8389-7244

Assistant at the Department of Anesthesiology, Resuscitation and Emergency Care of the Institute of Professional Education

Russian Federation, Samara

E. G. Bibikova

Samara State Medical University

Email: a.d.gureev@samsmu.ru
ORCID iD: 0009-0005-9392-1101
Russian Federation, Samara

A. V. Lunina

Samara State Medical University

Email: a.d.gureev@samsmu.ru
ORCID iD: 0000-0002-3182-2109
Russian Federation, Samara

References

  1. Sutton R.T., Pincock D., Baumgart D.C., et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digital Med. 2020;3(1):1–10. doi: 10.1038/s41746-020-0221-y.
  2. Patel J.L., Goyal R.K. Applications of artificial neural networks in medical science. Curr Clin Pharmacol. 2007;2(3):217–26. doi: 10.2174/157488407781668811.
  3. Smith T.R. Developmental Surveillance and Screening in the Electronic Health Record. Pediatr Clin North Am. 2016;63(5):933–43. doi: 10.1016/j.pcl.2016.06.014.
  4. Miziara I.D., Miziara C.S.M.G. Medical errors, medical negligence and defensive medicine: A narrative review. Clinics (Sao Paulo, Brazil). 2022;77:100053. doi: 10.1016/j.clinsp.2022.100053.
  5. Amirahmadi A., Ohlsson M., Etminani K. Deep learning prediction models based on EHR trajectories: A systematic review. J Biomed Inform. 2023;144:104430. doi: 10.1016/j.jbi.2023.104430.
  6. Rasmy L., Nigo M., Kannadath B.S., et al. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. Lancet. Digital Health. 2022;4(6):e415–25. doi: 10.1016/S2589-7500(22)00049-8.
  7. Tran K.A., Kondrashova O., Bradley A., et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Gen Med. 2021;13(1):152. doi: 10.1186/s13073-021-00968-x.
  8. Hansun S., Argha A., Liaw S.-T., et al. Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review. J Med Internet Res. 2023;25:e43154. doi: 10.2196/43154.
  9. Gürsoy E., Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. Multimed Syst. 2023;29(3):1603. doi: 10.1007/s00530-023-01083-0.
  10. LeCun Y., Bengio Y , Hinton G. Deep learning. Nature. 2015;521(7553):436–44. doi: 10.1038/nature14539.
  11. He Y., Zhao H., Wong S.T.C. Deep learning powers cancer diagnosis in digital pathology. Computerized Medical Imaging and Graphics: Official J Computer Med Imag Soc. 2021;88:101820. doi: 10.1016/j.compmedimag.2020.101820.
  12. Chuang W.-Y., Yu W.-H., Lee Y.-C., et al. Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma. Am J Pathol. 2022;192(12):1763–78. doi: 10.1016/j.ajpath.2022.08.006.
  13. Jiang X., Hu Z., Wang S., Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers. 2023;15(14):3608. doi: 10.3390/cancers15143608.
  14. Ting D.S.W., Pasquale L.R., Peng L., et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–75. doi: 10.1136/bjophthalmol-2018-313173.
  15. Kantidakis G., Hazewinkel A.-D., Fiocco M. Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal. Computat Mathemat Methods Med. 2022;2022:1176060. doi: 10.1155/2022/1176060.
  16. Koo K.C., Lee K.S., Kim S., et al. Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system. W J Urol. 2020;38(10):2469–76. doi: 10.1007/s00345-020-03080-8.
  17. Gohari M.R., Biglarian A., Bakhshi E., Pourhoseingholi M.A. Use of an artificial neural network to determine prognostic factors in colorectal cancer patients. APJCP. Asian Pacif J Cancer Prev. 2011;12(6):1469–72.
  18. Sakellaropoulos T., Vougas K., Narang S., et al. A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Rep. 2019;29(11):3367–73.e4. doi: 10.1016/j.celrep.2019.11.017.
  19. Lee M. Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature. Biology. 2023;12(7):893. doi: 10.3390/biology12070893.
  20. Reddy S. Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implement Sci: IS. 2024;19(1):27. doi: 10.1186/s13012-024-01357-9.
  21. Carl N., Schramm F., Haggenmüller S., et al. Large language model use in clinical oncology. NPJ Precision Oncol. 2024;8(1):240. doi: 10.1038/s41698-024-00733-4.
  22. Morley J., DeVito N.J., Zhang J. Generative AI for medical research. BMJ. (Clinical research ed.). 2023;382:1551. doi: 10.1136/bmj.p1551.
  23. Van De Sijpe G., Quintens C., Walgraeve K., et al. Overall performance of a drug-drug interaction clinical decision support system: quantitative evaluation and end-user survey. BMC. Medical informatics and decision making. 2022;22(1):48. doi: 10.1186/s12911-022-01783-z.
  24. Flores E., Martinez-Racaj L., Torreblanca R., et al. Clinical Decision Support System in laboratory medicine. Clin Chem Lab Med. 2024;62(7):1277–82. doi: 10.1515/cclm-2023-1239.
  25. Amici L.D., van Pelt M., Mylott L., et al. Clinical Decision Support as a Prevention Tool for Medication Errors in the Operating Room: A Retrospective Cross-Sectional Study. Anesth Analg. 2024;139(4):832–9. doi: 10.1213/ANE.0000000000007058.
  26. Njie G.J., Proia K.K., Thota A.B., et al. Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review. Am J Prev Med. 2015;49(5):784–95. doi: 10.1016/j.amepre.2015.04.006.
  27. Huang S., Liang Y., Li J., Li X. Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review. J Med Internet Res. 2023;25:e51024. doi: 10.2196/51024.
  28. Pichardo-Lowden A., Umpierrez G., Lehman E.B., et al. Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes. BMJ. Open Diab Res Care. 2021;9(1):e001557. doi: 10.1136/bmjdrc-2020-001557.
  29. Schwartz J.M., George M., Rossetti S.C., et al. Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study. JMIR. Human Factors. 2022;9(2):e33960. doi: 10.2196/33960.
  30. Chen H., Ma X., Rives H., et al. Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists. Laryngoscope. 2024;134(6):2799–804. doi: 10.1002/lary.31260.
  31. Wang J.X., Sullivan D.K., Wells A.J., et al. Neural Networks for Clinical Order Decision Support. AMIA Joint Summits on Translational Science proceedings. AMIA. Joint Summits Translat Sci. 2019;2019:315–24.
  32. Wang L., Zhang Z., Wang D., et al. Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review. Front Comput Sci. 2023. doi: 10.3389/fcomp.2023.1187299.
  33. Khan S., Khan H.U., Nazir S. Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing. Sci Rep. 2022;12(1):22377. doi: 10.1038/s41598-022-26090-5.
  34. Jing X., Himawan L., Law T. Availability and usage of clinical decision support systems (CDSSs) in office-based primary care settings in the USA. BMJ. Health & Care Inform. 2019;26(1):e100015. doi: 10.1136/bmjhci-2019-100015.
  35. Solomon J., Dauber-Decker K., Richardson S., et al. Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study. JMIR. Formative Res. 2023;7:e44065. doi: 10.2196/44065.
  36. Rahimi A.K., Pienaar O., Ghadimi M., et al. Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers. J Med Internet Res. 2024;26(1):e49655. doi: 10.2196/49655.
  37. Fraser H., Crossland D., Bacher I., et al. Comparison of Diagnostic and Triage Accuracy of Ada Health and WebMD Symptom Checkers, ChatGPT, and Physicians for Patients in an Emergency Department: Clinical Data Analysis Study. JMIR. mHealth and uHealth. 2023;11:e49995. doi: 10.2196/49995.
  38. Fernandes M., Vieira S.M., Leite F., et al. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artificial Intelligence Med. 2020;102:101762. doi: 10.1016/j.artmed.2019.101762.
  39. Katzman J.L., Shaham U., Cloninger A., et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC. Med Res Method. 2018;18(1):24. doi: 10.1186/s12874-018-0482-1.
  40. Johnson K.B., Wei W.-Q., Weeraratne D., et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Translat Sci. 2020;14(1):86. doi: 10.1111/cts.12884.
  41. Arora A., Alderman J.E., Palmer J., et al. The value of standards for health datasets in artificial intelligence-based applications. Nature Med. 2023;29(11):2929–38. Doi: 0.1038/s41591-023-02608-w.
  42. Batko K., Slezak A. The use of Big Data Analytics in healthcare. J Big Data. 2022;9(1):3. doi: 10.1186/s40537-021-00553-4.
  43. London A.J. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Center Rep. 2019;49(1):15–21. doi: 10.1002/hast.973.
  44. Juravle G., Boudouraki A., Terziyska M., Rezlescu C. Trust in artificial intelligence for medical diagnoses. Progress Brain Res. 2020;253:263–82. doi: 10.1016/bs.pbr.2020.06.006.
  45. Ibrahim A.M., Abdel-Aziz H.R., Mohamed H.A.H., et al. Balancing confidentiality and care coordination: challenges in patient privacy. BMC. Nursing. 2024;23(1):564. doi: 10.1186/s12912-024-02231-1.
  46. Mocydlarz-Adamcewicz M., Bajsztok B., Filip S., et al. Management of Onsite and Remote Communication in Oncology Hospitals: Data Protection in an Era of Rapid Technological Advances. J Personal Med. 2023;13(5):761. doi: 10.3390/jpm13050761.
  47. Abgrall G., Holder A.L., Chelly Dagdia Z., et al. Should AI models be explainable to clinicians? Critical Care. 2024;28(1):301. doi: 10.1186/s13054-024-05005-y.
  48. Palaniappan K., Lin E.Y.T., Vogel S. Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector. Healthcare (Basel, Switzerland). 2024;12(5):562. doi: 10.3390/healthcare12050562.
  49. Bottomley D., Thaldar D. Liability for harm caused by AI in healthcare: an overview of the core legal concepts. Front Pharmacol. 2023;14:1297353. doi: 10.3389/fphar.2023.1297353.
  50. Acosta J.N., Falcone G.J., Rajpurkar P., Topol E.J. Multimodal biomedical AI. Nature Med. 2022;28(9):1773–84. doi: 10.1038/s41591-022-01981-2.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».