Acta Scientific Computer Sciences

Review Article Volume 5 Issue 5

AI in Healthcare

Devarati Bagchi, Rimpa Bhowmick* and Divya Uppala

Department of Computer Sciences, India

*Corresponding Author: Rimpa Bhowmick, Department of Computer Sciences, India.

Received: February 24, 2023; Published: April 12, 2023

Abstract

AI has been a powerful emerging tool in the recent years creating revolutionary changes in the field of medicine with the usage of electronic health records, role in drug discovery and interactions. The increasing rise in software application in the field of medicine as well as digitalization of data fuel together the progress of development and usage of AI in medicine. AI applications have proven to be efficient in handling the pressing concerns faced by various health organizations.
This review paper discusses basics of AI-acquired algorithms in the predictions, diagnosis, assessment, clinical management of pathogenesis including a spectrum of various cancers.

Keywords: Artificial Intelligence; AI-led Drug Discovery; Patient Care; Machine Learning; Healthcare

References

  1. Seymour T., et al. “Electronic Health Records (EHR)”. American Journal of Health Sciences (AJHS)3 (2012): 201.
  2. Hufford MD DL. “Innovation in Medical Record Documentation: The Electronic Medical Record”. Uniformed Services Academy of Family Physicians (1999).
  3. Featherly K. “Eyes Wide Shut”. HIT Exchange 1 (2011): 18-21.
  4. Parmar C., et al. “Machine Learning methods for Quantitative Radiomic Biomarkers”. Scientific Report 5 (2015): 13087.
  5. Ferreira JR., et al. “Radiomics-based features for pattern recognition of lung cancer histopathology and metastases”. Computer Methods and Programs in Biomedicine 159 (2018): 23-30.
  6. Fujisawa Y., et al. “Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis”. British Journal of Dermatology 180 (2019): 373-381.
  7. Han SS., et al. “Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm”. Journal of Investigative Dermatology 138 (2018): 1529-1538.
  8. XR Li., et al. “PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer”. European Radiology 31 (2021): 5967-5979.
  9. SH Park., et al. “Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer”. Cancer Imaging 21 (2021).
  10. M Song., et al. “The value of MR-based radiomics in identifying residual disease in patients with carcinoma in situ after cervical conization”. Scientific Report 10 (2020): 19890.
  11. A Urushibara., et al. “Diagnosing uterine cervical cancer on a single T2-weighted image: comparison between deep learning versus radiologists”. European Journal of Radiology 135 (2021): 109471.
  12. Z Zhou., et al. “Quantitative PET imaging and clinical parameters as predictive factors for patients with cervical carcinoma: implications of a prediction model generated using multi-objective support vector machine learning”. Technology in Cancer Research and Treatment 19 (2020): 1533033820983804.
  13. JP Crandall., et al. “Repeatability of (18) FFDG PET radiomic features in cervical cancer”. The Journal of Nuclear Medicine 62 (2021): 707-715.
  14. D Al-Karawi., et al. “An evaluation of the effectiveness of image-based texture features extracted from static B-mode ultrasound images in distinguishing between benign and malignant ovarian masses”. Ultrasound Imaging 43 (2021): 124-138.
  15. RA Lupean., et al. “Radiomic analysis of MRI images is instrumental to the stratification of ovarian cysts”. Journal of Personalized Medicine 10 (2020): 14.
  16. Gunning S., et al. “Not all sepsis-associated acute kidney injury is the same: there may be an app for that”. Clinical Journal of the American Society of Nephrology 15 (2020): 1543-1545.
  17. Chaudhary K., et al. “Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury”. Clinical Journal of the American Society of Nephrology 15 (2020): 1557-1565.
  18. Ibrahim ZM., et al. “On classifying sepsis heterogeneity in the ICU: insight using machine learning”. Journal of the American Medical Informatics Association 27 (2020): 437-443.
  19. Perng JW., et al. “Mortality prediction of septic patients in the emergency department based on machine learning”. Journal of Clinical Medicine (2019) 8:1906.
  20. Manne R and Kantheti SC. “Application of Artificial Intelligence in Healthcare Chances and Challenges”. Current Journal of Applied Science and Technology6 (2021): 78-89.
  21. Gerke Sara., et al. “Ethical and Legal Challenges of Artificial Intelligence-Driven Health Care”. SSRN Electronic Journal (2020): 10.2139/ssrn.3570129.
  22. Binczyk F., et al. “Radiomics and artificial intelligence in lung cancer screening”. Translational Lung Cancer Research2 (2021): 1186-1199.
  23. Khanna D. “Use Of Artificial Intelligence in Health Care and Medicine”. Novateur (2018).
  24. Nicholson Price W. “Artificial Intelligence in Health Care: Applications And Legal Implications”. The Scitech Lawyer (2017): 599.
  25. Pham T., et al. “Predicting healthcare trajectories from medical records: A deep learning approach”. Journal of Biomedical Informatics 69 (2017): 218-229.
  26. Secinaro S., et al. “The role of artificial intelligence in healthcare: a structured literature review”. BMC Medical Informatics and Decision Making1 (2021).
  27. Shah R and Chircu A. “IOT and AI in healthcare : A Systematic Literature Review”. (2018).
  28. Shrestha P., et al. “A systematic review on the use of artificial intelligence in gynecologic imaging-Backgroung, state of the art and future directions”. Gynecologic Oncology (2022).
  29. Wang J and Jiang C. “Chapter 68-1 Machine learning paradigms in Wireless Network Association”. Springer Science and Business Media LLC. (2018).
  30. Wu M., et al. “Artificial Intelligence for Clinical Decision Support in Sepsis”. Frontiers in Medicine 8 (2021).
  31. Miller DD and Brown EW. “Artificial intelligence in medical practice: The question to the answer?” The American Journal of Medicine 131 (2018): 129-133.

Citation

Citation: Devarati Bagchi, Rimpa Bhowmick and Divya Uppala. “AI in Healthcare". Acta Scientific Computer Sciences 5.5 (2023): 56-63.

Copyright

Copyright: © 2023 Devarati Bagchi, Rimpa Bhowmick and Divya Uppala. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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