New Opportunities and Challenges for Health Professionals in the era of Artificial Intelligence – Review
Keywordshealth, artificial intelligence, infectious diseases, radiology, dermatology, surgery
Introduction and purpose: Modern medical knowledge has grown to a vastness incomprehensible for a single health professional to learn and accommodate. The usage of modern information technologies comes to help, one of them being artificial intelligence, a branch of computer science aimed at developing solutions to perform tasks similar to the human brain, but more efficient and complex, without actual human intervention. The goal of this review is to provide reader with the knowledge how artificial intelligence is applied in various branches of medicine.
Brief description of the state of knowledge: In the fields of infectious diseases, including COVID-19 diagnostics, radiology, dermatology and surgery, works lean toward the statement, which suspect application of AI is beneficial for medical practitioners. Programs help to develop statistical models for virus spreading and the creation of antiviral solutions. The radiological application involves the analysis of images to aid radiologists in diagnosing certain features, similarly to dermatology, where eg. AI can identify malignancy of skin nevi. In the department of surgery, predictive algorithms can help in choosing operation methods and improve outcomes.
Conclusions: Usage of AI assistance in the medical field has proven to be successful, but it is yet to be commonly encountered in everyday work. Programs need to be further developed and made more approachable to users without expertise in the IT field. AI may also prove useful in the process of education of health professionals.
J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955,” AI Mag., vol. 27, no. 4, pp. 12–12, Dec. 2006, doi: 10.1609/AIMAG.V27I4.1904.
S. J. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach, 4th US ed.” http://aima.cs.berkeley.edu/ (accessed Sep. 08, 2022).
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/NATURE14539.
T. B. Murdoch and A. S. Detsky, “The inevitable application of big data to health care,” JAMA, vol. 309, no. 13, pp. 1351–1352, Apr. 2013, doi: 10.1001/JAMA.2013.393.
R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr., vol. 14, no. 4, pp. 337–339, Aug. 2020, doi: 10.1016/j.dsx.2020.04.012.
Y.-L. Xia, W. Li, Y. Li, X.-L. Ji, Y.-X. Fu, and S.-Q. Liu, “A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2,” Comput. Math. Methods Med., vol. 2021, p. 9997669, 2021, doi: 10.1155/2021/9997669.
X. Fan, N. Xue, Z. Han, C. Wang, H. Ma, and Y. Lu, “Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning,” J. Healthc. Eng., vol. 2021, p. 6128260, 2021, doi: 10.1155/2021/6128260.
S. Panjwani et al., “Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes,” Biotechnol. Prog., vol. 37, no. 3, p. e3135, May 2021, doi: 10.1002/btpr.3135.
Z. Li, Y. Yao, X. Cheng, W. Li, and T. Fei, “An in silico drug repositioning workflow for host-based antivirals,” STAR Protoc., vol. 2, no. 3, p. 100653, Sep. 2021, doi: 10.1016/j.xpro.2021.100653.
M. Wardeh, M. S. C. Blagrove, K. J. Sharkey, and M. Baylis, “Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations,” Nat. Commun., vol. 12, no. 1, p. 3954, Jun. 2021, doi: 10.1038/s41467-021-24085-w.
R. L. Kumar, F. Khan, S. Din, S. S. Band, A. Mosavi, and E. Ibeke, “Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction,” Front. Public Heal., vol. 9, p. 744100, 2021, doi: 10.3389/fpubh.2021.744100.
M. M. Rahman, F. Khatun, A. Uzzaman, S. I. Sami, M. A.-A. Bhuiyan, and T. S. Kiong, “A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic,” Int. J. Heal. Serv. Planning, Adm. Eval., vol. 51, no. 4, pp. 446–461, Oct. 2021, doi: 10.1177/00207314211017469.
J. Chen and K. C. See, “Artificial Intelligence for COVID-19: Rapid Review,” J. Med. Internet Res., vol. 22, no. 10, p. e21476, Oct. 2020, doi: 10.2196/21476.
H. Zhang et al., “Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov,” Interdiscip. Sci. Comput. Life Sci., vol. 12, no. 3, pp. 368–376, Sep. 2020, doi: 10.1007/s12539-020-00376-6.
E. Dikici, M. Bigelow, L. M. Prevedello, R. D. White, and B. S. Erdal, “Integrating AI into radiology workflow: levels of research, production, and feedback maturity,” https://doi.org/10.1117/1.JMI.7.1.016502, vol. 7, no. 1, p. 016502, Feb. 2020, doi: 10.1117/1.JMI.7.1.016502.
O. S. Pianykh et al., “Continuous learning AI in radiology: Implementation principles and early applications,” Radiology, vol. 297, no. 1, pp. 6–14, Oct. 2020, doi: 10.1148/RADIOL.2020200038/ASSET/IMAGES/LARGE/RADIOL.2020200038.FIG4.JPEG.
M. H. Rezazade Mehrizi, P. van Ooijen, and M. Homan, “Applications of artificial intelligence (AI) in diagnostic radiology: a technography study,” Eur. Radiol., vol. 31, no. 4, pp. 1805–1811, Apr. 2021, doi: 10.1007/S00330-020-07230-9/TABLES/2.
G. Choy et al., “Current applications and future impact of machine learning in radiology,” Radiology, vol. 288, no. 2, pp. 318–328, Aug. 2018, doi: 10.1148/RADIOL.2018171820/ASSET/IMAGES/LARGE/RADIOL.2018171820.FIG8.JPEG.
T. Cai et al., “Natural language processing technologies in radiology research and clinical applications,” Radiographics, vol. 36, no. 1, pp. 176–191, Jan. 2016, doi: 10.1148/RG.2016150080/ASSET/IMAGES/LARGE/RG.2016150080.TBL1.JPEG.
K. Bera, N. Braman, A. Gupta, V. Velcheti, and A. Madabhushi, “Predicting cancer outcomes with radiomics and artificial intelligence in radiology,” Nat. Rev. Clin. Oncol., vol. 19, no. 2, pp. 132–146, Feb. 2022, doi: 10.1038/S41571-021-00560-7.
H. Park et al., “Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer,” Clin. Cancer Res., vol. 24, no. 19, pp. 4705–4714, Oct. 2018, doi: 10.1158/1078-0432.CCR-17-3783.
C. Curtis, C. Liu, T. J. Bollerman, and O. S. Pianykh, “Machine Learning for Predicting Patient Wait Times and Appointment Delays,” J. Am. Coll. Radiol., vol. 15, no. 9, pp. 1310–1316, Sep. 2018, doi: 10.1016/J.JACR.2017.08.021.
A. Gomolin, E. Netchiporouk, R. Gniadecki, and I. V. Litvinov, “Artificial Intelligence Applications in Dermatology: Where Do We Stand?,” Front. Med., vol. 7, p. 100, Mar. 2020, doi: 10.3389/FMED.2020.00100/XML/NLM.
F. Xie, H. Fan, Y. Li, Z. Jiang, R. Meng, and A. Bovik, “Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model,” IEEE Trans. Med. Imaging, vol. 36, no. 3, pp. 849–858, Mar. 2017, doi: 10.1109/TMI.2016.2633551.
C. Yu et al., “Acral melanoma detection using a convolutional neural network for dermoscopy images,” PLoS One, vol. 13, no. 3, p. e0193321, Mar. 2018, doi: 10.1371/JOURNAL.PONE.0193321.
M. P. Pour, H. Seker, and L. Shao, “Automated lesion segmentation and dermoscopic feature segmentation for skin cancer analysis,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 640–643, Sep. 2017, doi: 10.1109/EMBC.2017.8036906.
M. H. Jafari, S. Samavi, N. Karimi, S. M. R. Soroushmehr, K. Ward, and K. Najarian, “Automatic detection of melanoma using broad extraction of features from digital images,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2016-October, pp. 1357–1360, Oct. 2016, doi: 10.1109/EMBC.2016.7590959.
L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng., vol. 64, no. 9, pp. 2065–2074, Sep. 2017, doi: 10.1109/TBME.2017.2712771.
J. L. García Arroyo and B. García Zapirain, “Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis,” Comput. Biol. Med., vol. 44, no. 1, pp. 144–157, Jan. 2014, doi: 10.1016/J.COMPBIOMED.2013.11.002.
M. H. Jafari, E. Nasr-Esfahani, N. Karimi, S. M. R. Soroushmehr, S. Samavi, and K. Najarian, “Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma,” Int. J. Comput. Assist. Radiol. Surg., vol. 12, no. 6, pp. 1021–1030, Jun. 2017, doi: 10.1007/S11548-017-1567-8/TABLES/1.
M. Lingala et al., “Fuzzy logic color detection: Blue areas in melanoma dermoscopy images,” Comput. Med. Imaging Graph., vol. 38, no. 5, pp. 403–410, Jul. 2014, doi: 10.1016/J.COMPMEDIMAG.2014.03.007.
S. Souza and J. M. Abe, “Nevus and Melanoma Paraconsistent Classification,” Stud. Health Technol. Inform., vol. 207, pp. 244–250, 2014, doi: 10.3233/978-1-61499-474-9-244.
P. Tschandl, H. Kittler, and G. Argenziano, “A pretrained neural network shows similar diagnostic accuracy to medical students in categorizing dermatoscopic images after comparable training conditions,” Br. J. Dermatol., vol. 177, no. 3, pp. 867–869, Sep. 2017, doi: 10.1111/BJD.15695.
S. Afifi, H. Gholamhosseini, and R. Sinha, “SVM classifier on chip for melanoma detection,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Int. Conf., vol. 2017, pp. 270–274, Sep. 2017, doi: 10.1109/EMBC.2017.8036814.
T. B. Jutzi et al., “Artificial Intelligence in Skin Cancer Diagnostics: The Patients’ Perspective,” Front. Med., vol. 7, p. 233, Jun. 2020, doi: 10.3389/FMED.2020.00233/BIBTEX.
D. N. Anggraini Ningrum et al., “Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection,” J. Multidiscip. Healthc., vol. 14, pp. 877–885, 2021, doi: 10.2147/JMDH.S306284.
A. Hekler et al., “Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images,” Eur. J. Cancer, vol. 118, pp. 91–96, Sep. 2019, doi: 10.1016/J.EJCA.2019.06.012.
S. S. Han et al., “Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network,” JAMA dermatology, vol. 156, no. 1, pp. 29–37, Jan. 2020, doi: 10.1001/JAMADERMATOL.2019.3807.
A. Udrea et al., “Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms,” J. Eur. Acad. Dermatology Venereol., vol. 34, no. 3, pp. 648–655, Mar. 2020, doi: 10.1111/JDV.15935.
T. E. Sangers, T. Nijsten, and M. Wakkee, “Mobile health skin cancer risk assessment campaign using artificial intelligence on a population-wide scale: a retrospective cohort analysis,” J. Eur. Acad. Dermatology Venereol., vol. 35, no. 11, pp. e772–e774, Nov. 2021, doi: 10.1111/JDV.17442.
A. Pampín-Franco, R. Gamo-Villegas, U. Floristán-Muruzábal, F. J. Pinedo-Moraleda, E. Pérez-Fernández, and J. L. López-Estebaranz, “Melanocytic lesions with peripheral globules: results of an observational prospective study in 154 high-risk melanoma patients under digital dermoscopy follow-up evaluated with reflectance confocal microscopy,” J. Eur. Acad. Dermatology Venereol., vol. 35, no. 5, pp. 1133–1142, May 2021, doi: 10.1111/JDV.17105.
M. A. Marchetti et al., “Computer Algorithms Show Potential for Improving Dermatologists’ Accuracy to Diagnose Cutaneous Melanoma; Results of ISIC 2017,” J. Am. Acad. Dermatol., vol. 82, no. 3, p. 622, Mar. 2020, doi: 10.1016/J.JAAD.2019.07.016.
W. Sondermann et al., “Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data,” Eur. J. Cancer, vol. 119, pp. 30–34, Sep. 2019, doi: 10.1016/J.EJCA.2019.07.009.
A. X. Du, S. Emam, and R. Gniadecki, “Review of Machine Learning in Predicting Dermatological Outcomes,” Front. Med., vol. 7, p. 266, Jun. 2020, doi: 10.3389/FMED.2020.00266/XML/NLM.
K. A. Papp, A. M. Soliman, N. Done, C. Carley, E. Lemus Wirtz, and L. Puig, “Deterioration of Health-Related Quality of Life After Withdrawal of Risankizumab Treatment in Patients with Moderate-to-Severe Plaque Psoriasis: A Machine Learning Predictive Model,” Dermatol. Ther. (Heidelb)., vol. 11, no. 4, pp. 1291–1304, Aug. 2021, doi: 10.1007/S13555-021-00550-8/FIGURES/4.
T. Zhang and Y. Nie, “Prediction of the Risk of Alopecia Areata Progressing to Alopecia Totalis and Alopecia Universalis: Biomarker Development with Bioinformatics Analysis and Machine Learning,” Dermatology, vol. 238, no. 2, pp. 386–396, Mar. 2022, doi: 10.1159/000515764.
G. Damiani et al., “Predicting Secukinumab Fast-Responder Profile in Psoriatic Patients: Advanced Application of Artificial-Neural-Networks (ANNs),” J. Drugs Dermatol., vol. 19, no. 12, pp. 1241–1246, Dec. 2020, doi: 10.36849/JDD.2020.5006.
T. J. Loftus et al., “Artificial Intelligence and Surgical Decision-making,” JAMA Surg., vol. 155, no. 2, pp. 148–158, Feb. 2020, doi: 10.1001/JAMASURG.2019.4917.
V. Bellini et al., “Current Applications of Artificial Intelligence in Bariatric Surgery,” Obes. Surg. 2022 328, vol. 32, no. 8, pp. 2717–2733, May 2022, doi: 10.1007/S11695-022-06100-1.
D. A. Hashimoto, G. Rosman, D. Rus, and O. R. Meireles, “Artificial Intelligence in Surgery: Promises and Perils,” Ann. Surg., vol. 268, no. 1, pp. 70–76, Jul. 2018, doi: 10.1097/SLA.0000000000002693.
M. Bektaş et al., “Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives,” Obes. Surg. 2022 328, vol. 32, no. 8, pp. 2772–2783, Jun. 2022, doi: 10.1007/S11695-022-06146-1.
A. Eresen et al., “Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: A pilot study,” Cancer Imaging, vol. 20, no. 1, pp. 1–9, Apr. 2020, doi: 10.1186/S40644-020-00308-Z/TABLES/2.
R. D. Dias, J. A. Shah, and M. A. Zenati, “Artificial intelligence in cardiothoracic surgery,” Minerva Cardioangiol., vol. 68, no. 5, pp. 532–538, Oct. 2020, doi: 10.23736/S0026-4725.20.05235-4.
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