The Role of Artificial Intelligence in Treatment and Diagnosis in Healthcare

Shekhar Singh; Vishal Rai; Ajay Yadav; Akanksha Kanojia; Sanjay Kumar Srivastava

doi:10.55544/jrasb.3.4.2

Authors

Shekhar Singh Department of Pharmacy, Suyash Institute of Pharmacy, Gorakhpur, Uttar Pradesh, INDIA.
Vishal Rai Department of Pharmacy, Suyash Institute of Pharmacy, Gorakhpur, Uttar Pradesh, INDIA.
Ajay Yadav Department of Pharmacy, Suyash Institute of Pharmacy, Gorakhpur, Uttar Pradesh, INDIA.
Akanksha Kanojia Department of Pharmacy, Suyash Institute of Pharmacy, Gorakhpur, Uttar Pradesh, INDIA.
Sanjay Kumar Srivastava Department of Pharmacy, Suyash Institute of Pharmacy, Gorakhpur, Uttar Pradesh, INDIA.

DOI:

https://doi.org/10.55544/jrasb.3.4.2

Keywords:

Artificial Intelligence (AI)

Abstract

Technology, specifically artificial intelligence (AI) is gradually but progressively creeping into the health sector and it’s perhaps the one that has been revolutionised most in diagnosis and treatment. This review brings out discussions on the practices of AI technologies in medical, the pros and the cons. First of all, an endeavour is made to elucidate the meaning of the term AI and its utilization in the field of healthcare. The specific AI techniques are described comprehensively focusing on the machine learning, deep learning, and natural language processing methods to be used in the project The role of multiple types of data in AI includes the EHR, medical images, and genomics data. Self-diagnosis: AI is improving the diagnosis approaches in the radiology and pathology fields and predicting the early-stage disease with better results in most of the cases, and enhancing the identification of genetic diseases. As for treatment, the enhancement of the use of AI has had an impact on issues such as; Prescribing and recommending drugs according to the characteristics of the patients, smart drug administration and management, robotic surgeries and simulations. Discussions are made using concrete and successful implementation of AI in cancer, cardiovascular, neurological and infectious diseases for the purpose of elucidating particular results. This also has to do with the ethical and legal problems like who has the liability to determine in the instance of complicated problems, patients’ information discretion, data privacy, and other legalities. In this article, we briefly mention the prosaic matters of AI, which deals with the engineering aspects of establishing AI such as the aspect of data and the ways and means of checking them and the interdisciplinary character of it. Concerning future developments, additional technologies like AI and connected devices in the field of health care, interdisciplinary at national and international level as well as data sharing is emphasized. Thus. AI has a very great perspective in healthcare, particularly in diagnostics and treatment of diseases due to the probability of increasing the level of accuracy, efficacy, and personalization. Despite these, they are tangible objectives with major challenges and require cooperation between nations with proper handling of Artificial Intelligence to practice clinical medication.

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References

Russakovsky, O., Deng, J., Su, H., et al. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Esteva, A., Kuprel, B., Novoa, R.A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Jha, S., Topol, E.J. (2020). Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists. JAMA, 323(2), 135-136.

Deo, R.C. (2015). Machine Learning in Medicine. Circulation, 132(20), 1920-1930.

Obermeyer, Z., & Emanuel, E.J. (2016). Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine, 375(13), 1216-1219.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347-1358.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Esteva, A., Kuprel, B., Novoa, R.A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Wang, Y., Wang, L., Rastegar-Mojarad, M., et al. (2018). Clinical information extraction applications: A literature review. Journal of Biomedical Informatics, 77, 34-49.

Ford, E., & Price, C. (2016). Using NLP to extract research priority related information from hospital patient records. Journal of Biomedical Informatics, 60, 164-170.

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.

Bates, D.W., Saria, S., Ohno-Machado, L., et al. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.

Litjens, G., Kooi, T., Bejnordi, B.E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

Gulshan, V., Peng, L., Coram, M., et al. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402-2410.

Ashley, E.A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507-522.

Collins, F.S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795.

Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574-582.

Rajpurkar, P., Irvin, J., Zhu, K., et al. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.

McBee, M.P., Awan, O.A., Colucci, A.T., et al. (2018). Deep learning in radiology. Academic Radiology, 25(11), 1472-1480.

Tang, A., Tam, R., Cadrin-Chênevert, A., et al. (2018). Canadian Association of Radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists Journal, 69(2), 120-135.

Campanella, G., Hanna, M.G., Geneslaw, L., et al. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(8), 1301-1309.

Pantanowitz, L., Quiroga-Garza, G.M., Bien, L., et al. (2020). An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. The Lancet Digital Health, 2(8), e407-e416.

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., et al. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 318(22), 2199-2210.

Litjens, G., Kooi, T., Bejnordi, B.E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Obermeyer, Z., & Emanuel, E.J. (2016). Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine, 375(13), 1216-1219.

Collins, F.S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795.

McKinney, S.M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.

Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., et al. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65-69.

Poplin, R., Varadarajan, A.V., Blumer, K., et al. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158-164.

Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.

Obermeyer, Z., & Emanuel, E.J. (2016). Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine, 375(13), 1216-1219.

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

U.S. Food and Drug Administration. (2020). Artificial Intelligence and Machine Learning in Software as a Medical Device. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device

WIPO. (2019). WIPO Technology Trends 2019: Artificial Intelligence. World Intellectual Property Organization. Retrieved from https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf

Beam, A.L., & Kohane, I.S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.

Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

Mak, W.W., Chio, F.H., Chan, A.T., Lui, W.W., & Wu, E.K. (2017). The efficacy of Internet-based mindfulness training and cognitive-behavioral training with telephone support in the enhancement of mental health among college students and young working adults: randomized controlled trial. Journal of Medical Internet Research, 19(3), e6737.

Gunning, D., & Aha, D. (2019). DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine, 40(2), 44-58.

He, J., Baxter, S.L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36.

Global Alliance for Genomics and Health. (2020). Framework for Responsible Sharing of Genomic and Health-Related Data. Retrieved from https://www.ga4gh.org/genomic-data-toolkit/framework-for-responsible-sharing-of-genomic-and-health-related-data

The Cancer Imaging Archive (TCIA). (2021). The Cancer Imaging Archive: Data Sharing for the Greater Good. Retrieved from https://www.cancerimagingarchive.net/