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Poalelungi, D.G.; Musat, C.L.; Fulga, A.; Neagu, M.; Neagu, A.I.; Piraianu, A.I.; Fulga, I. Role of Artificial Intelligence in Healthcare. Encyclopedia. Available online: https://encyclopedia.pub/entry/53864 (accessed on 03 December 2024).
Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, et al. Role of Artificial Intelligence in Healthcare. Encyclopedia. Available at: https://encyclopedia.pub/entry/53864. Accessed December 03, 2024.
Poalelungi, Diana Gina, Carmina Liana Musat, Ana Fulga, Marius Neagu, Anca Iulia Neagu, Alin Ionut Piraianu, Iuliu Fulga. "Role of Artificial Intelligence in Healthcare" Encyclopedia, https://encyclopedia.pub/entry/53864 (accessed December 03, 2024).
Poalelungi, D.G., Musat, C.L., Fulga, A., Neagu, M., Neagu, A.I., Piraianu, A.I., & Fulga, I. (2024, January 16). Role of Artificial Intelligence in Healthcare. In Encyclopedia. https://encyclopedia.pub/entry/53864
Poalelungi, Diana Gina, et al. "Role of Artificial Intelligence in Healthcare." Encyclopedia. Web. 16 January, 2024.
Role of Artificial Intelligence in Healthcare
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Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients’ outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. 

artificial intelligence machine learning deep learning clinical applications diagnosis

1. Introduction

Artificial intelligence (AI) is increasingly being used as a virtual tool in many countries around the world. With its ability to mimic human cognitive functions, AI has revolutionized industries, improved efficiency, and unlocked new possibilities. During the past few years, governments have adopted a variety of smart applications that can use AI and its subsets provide predictions and recommendations in various fields, such as healthcare, finance, agriculture, education, social media, and data security.
Since the outbreak of COVID-19 in 2019, AI technologies have experienced accelerated adoption and utilization across various domains within the healthcare sector. In response to the pandemic, AI has emerged as a valuable tool and is being used for disease detection and diagnosis, medical imaging and analysis, treatment planning and personalized medicine, drug discovery and development, predictive analytics, and risk assessment. In 2018, Loh E. [1] stated that AI has the potential to significantly transform physicians’ roles and revolutionize the practice of medicine, and it is important for all doctors, in particular those in positions of leadership within the health system, to anticipate the potential changes, forecast their impact and make strategic plans for the medium to long term. In contrast, in 2021, Mistry C. et al. [2] assessed that the necessity for deploying advanced digital devices has become a requirement to offer augmented customer satisfaction, permitting tracking, checking the health status, and achieving better drug adherence.
The field of AI is continuously evolving and researchers are exploring various avenues to create intelligent systems with different capabilities. The authors employed a visual representation, in the form of Figure 1, to illustrate the diverse subtypes of AI. Table 1 provides an overview of the definitions of terms related to AI and their integration within the healthcare sector.
Figure 1. Illustration of the AI subtypes.

2. Disease Detection and Diagnosis and Medical Imaging

The application of AI within the diagnostic process supporting medical specialists could be of great value for the healthcare sector and the patients’ overall well-being [22]. The fundamental goal of the diagnosis of a disease lies in determining whether a patient is affected by a disease or not [23]. The first step in the diagnostic process involves obtaining a complete medical history and conducting a physical examination. For instance, a technique can use sound analysis to recognize COVID-19 from different respiratory sounds, e.g., cough, breathing, and voice [24]. Additionally, for a precise diagnosis, AI algorithms can be used for the analysis of medical scans and pathology images. Imaging applications include the determination of ejection fraction from echocardiograms [25], the detection and volumetric quantification of lung nodules from radiographs [26], and the detection and quantification of breast densities via mammography [27]. Imaging applications in pathology include an FDA-cleared system for whole-slide imaging (WSI) and their integration into a laboratory offers many benefits over light microscopy [28].

3. Treatment Planning and Personalized Medicine

AI tools have the ability to analyze large amounts of data and detect patterns. Therefore, they can make predictions for efficient and personalized treatment strategies. Personalized medicine, as an extension of medical sciences, uses practice and medical decisions to deliver customized healthcare services to patients [29]. For example, CURATE.AI is an AI-derived platform that maps the relationship between an intervention intensity (input-drug) and a phenotypic result (output) for an individual, based exclusively on that individual’s data, creating a profile, which serves as a map to predict the outcome for a specified input and to recommend the intervention intensity that will provide the best result [30].

4. Drug Discovery and Development

The use of AI has been increasing in the pharmaceutical industry, and as a result, it has reduced the human workload as well as achieved targets in a short period of time [31]. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design [32][33]. In January 2023, Insilico Medicine announced an encouraging topline readout of its phase 1 safety and pharmacokinetics trial of the molecule INS018_055, designed by AI for idiopathic pulmonary fibrosis, a progressive disease that causes scarring of the lungs [34].

5. Predictive Analytics and Risk Assessment

Disease risk assessment is the process of evaluating a person’s probability of developing certain diseases, based on risk factors such as genetic predispositions, environmental exposures, and lifestyle choices. AI techniques have been adopted to address the various steps involved in clinical genomic analysis—including variant calling, genome annotation, variant classification, and phenotype-to-genotype correspondence—and perhaps eventually they can also be applied to genotype-to-phenotype predictions [35]. Moreover, Ramazzotti et al. accomplished a successful prognosis prediction for 27 out of 36 cancers by employing AI to analyze various types of biological data such as RNA expression, point mutations, DNA methylation, and omics data of copy number variation. The data used for analysis was sourced from The Cancer Genome Atlas (TCGA) [36].

References

  1. Loh, E. Medicine and the rise of the robots: A qualitative review of recent advances of artificial intelligence in health. BMJ Lead. 2018, 2, 59–63.
  2. Mistry, C.; Thakker, U.; Gupta, R.; Obaidat, M.S.; Tanwar, S.; Kumar, N.; Rodrigues, J.J.P.C. MedBlock: An AI-Enabled and Blockchain-Driven Medical Healthcare System for COVID-19. In Proceedings of the IEEE International Conference Communication, Montreal, QC, Canada, 14–23 June 2021; pp. 1–6.
  3. Turing, A.M. I. Computing machinery and intelligence. Mind 1950, 236, 433–460.
  4. Salto-Tellez, M.; Maxwell, P.; Hamilton, P. Artificial intelligence-the third revolution in pathology. Histopathology 2019, 74, 372–376.
  5. Kaplan, A.; Haenlein, M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 2019, 62, 15–25.
  6. Bini, S.A. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J. Arthroplast. 2018, 33, 2358–2361.
  7. Naylor, C.D. On the prospects for a (deep) learning health care system. JAMA 2018, 320, 1099–1100.
  8. Alpaydin, E. Introduction to Machine Learning, 3rd ed.; The MIT Press: Cambridge, MA, USA, 2014; p. 3.
  9. Javaid, M.; Haleem, A.; Singh, R.P.; Suman, R.; Rab, S. Significance of machine learning in healthcare: Features, pillars and applications. Int. J. Intell. Netw. 2022, 3, 58–73.
  10. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
  11. Liu, X.; Faes, L.; Kale, A.U.; Wagner, S.K.; Fu, D.J.; Bruynseels, A.; Mahendiran, T.; Moraes, G.; Shamdas, M.; Kern, C.; et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digit. Health 2019, 1, e271–e297.
  12. Liddy, E.D. Natural Language Processing. In Encyclopedia of Library and Information Science, 2nd ed.; Marcel Decker, Inc.: New York, NY, USA, 2001.
  13. Iroju, O.G.; Olaleke, J.O. A Systematic Review of Natural Language Processing in Healthcare. Int. J. Inf. Technol. Comput. Sci. 2015, 7, 44–50.
  14. Bann, S.; Khan, M.; Hernandez, J.; Munz, Y.; Moorthy, K.; Datta, V.; Rockall, T.; Darzi, A. Robotics in Surgery. J. Am. Coll. Surg. 2003, 196, 784–795.
  15. Hussain, A.; Malik, A.; Halim, M.U.; Ali, A.M. The use of robotics in surgery: A review. Int. J. Clin. Pract. 2014, 68, 1376–1382.
  16. Jain, A.K.; Mao, J.; Mohiuddin, K.M. Artificial neural networks: A tutorial. Computer 1996, 29, 31–44.
  17. Papik, K.; Molnár, B.; Schaefer, R.; Dombóvári, Z.; Tulassay, Z.; Féher, J. Application of neural networks in medicine—A review. Med. Sci. Monit. 1998, 4, 538–546.
  18. Abraham, T.H. Integrating mind and brain: Warren S. McCulloch, cerebral localization, and experimental epistemology. Endeav. 2003, 27, 32–36.
  19. Itchhaporia, D.; Snow, P.B.; Almassy, R.J.; Oetgen, W.J. Artificial neural networks: Current status in cardiovascular medicine. J. Am. Coll. Cardiol. 1996, 28, 515–521.
  20. Baxt, W.G. Application of artificial neural networks to clinical medicine. Lancet 1995, 346, 1135–1138.
  21. Lisboa, P.J.; Taktak, A.F. The use of artificial neural networks in decision support in cancer: A systematic review. Neural Netw. 2006, 19, 408–415.
  22. Mirbabaie, M.; Stieglitz, S.; Frick, N.R.J. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol. 2021, 11, 693–731.
  23. Ransohoff, D.F.; Feinstein, A.R. Problems of Spectrum and Bias in Evaluating the Efficacy of Diagnostic Tests. N. Engl. J. Med. 1978, 299, 926–930.
  24. Lella, K.K.; Pja, A. A literature review on COVID-19 disease diagnosis from respiratory sound data. AIMS Bioeng. 2021, 8, 140–153.
  25. Asch, F.M.; Abraham, T.; Jankowski, M.; Cleve, J.; Adams, M.; Romano, N.; Polivert, N.; Hong, H.; Lang, R. Accuracy and reproducibility of a novel artificial intelligence deep learning-based algorithm for automated calculation of ejection fraction in echocardiography. J. Am. Coll. Cardiol. 2019, 73 (Suppl. S1), 1447.
  26. Retson, T.A.; Besser, A.H.; Sall, S.; Golden, D.; Hsiao, A. Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging. J. Thorac. Imaging 2019, 34, 192–201.
  27. Le, E.P.V.; Wang, Y.; Huang, Y.; Hickman, S.; Gilbert, F.J. Artificial intelligence in breast imaging. Clin. Radiol. 2019, 74, 357–366.
  28. Evans, A.J.; Bauer, T.W.; Bui, M.M.; Cornish, T.C.; Duncan, H.; Glassy, E.F.; Hipp, J.; McGee, R.S.; Murphy, D.; Myers, C.; et al. US Food and Drug Administration approval of whole slide imaging for primary diagnosis: A key milestone is reached and new questions are raised. Arch. Pathol. Lab. Med. 2018, 142, 1383–1387.
  29. Awwalu, J.; Garba, A.G.; Ghazvini, A.; Atuah, R. Artificial intelligence in personalized medicine application of AI algorithms in solving personalized medicine problems. Int. J. Comput. Theory Eng. 2015, 7, 439–443.
  30. Blasiak, A.; Khong, J.; Kee, T. CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence. Slas Technol. 2020, 25, 95–105.
  31. Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R.K. Artificial intelligence in drug discovery and development. Drug Discov. Today 2021, 26, 80–93.
  32. Mak, K.K.; Pichika, M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discov. Today 2019, 24, 773–780.
  33. Sellwood, M.A. Artificial intelligence in drug discovery. Fut. Sci. 2018, 10, 2025–2028.
  34. Arnold, C. Inside the nascent industry of AI-designed drugs. Nat. Med. 2023, 29, 1292–1295.
  35. Dias, R.; Torkamani, A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019, 11, 70.
  36. Ramazzotti, D.; Lal, A.; Wang, B.; Batzoglou, S.; Sidow, A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat. Commun. 2018, 9, 4453.
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