AI Powered Bioinformatics - Expediting Diagnostic Testing: A Survey
DOI:
https://doi.org/10.59992/IJCI.2024.v3n9p2Keywords:
Artificial Intelligence, Diagnosing, AI Powered BioinformaticsAbstract
Research has demonstrated the positive impact of artificial intelligence and Bioinformatics in the field of clinical diagnosis. The integration of AI methodologies into bioinformatics has opened new avenues for breakthroughs in genomics, proteomics, and personalized medicine. The document emphasizes the role of AI in early disease detection, improving patient outcomes, and enhancing healthcare systems by avoiding the need for expensive and time-consuming operations as illnesses worsen. The methodology section provides insights into the approach utilized, including the review of 30 articles from highly regarded journals about AI and bioinformatics that expedite diagnostic testing in the medical field. using survey to gather information and divide it into sub-sections focusing on diagnostic cancer diseases, COVID-19, and genetic and chronic diseases. The survey gathered 52 responses, and the results revealed significant agreements with the findings in the papers, particularly in the importance of developing novel biosensors and diagnostic tools for rapid and accessible detection of SARS-CoV-2, and the potential of AI in laboratory settings, pharmaceutical industry, and disease diagnosis. Overall, the document provides a comprehensive overview of the transformative role of AI in bioinformatics, emphasizing its potential to revolutionize disease diagnosis, treatment, and public health decision-making, while also addressing the challenges and opportunities associated with the integration of AI technologies in the healthcare industry. The rigorous methodology and alignment of survey results with the research findings validate the significance of AI-powered bioinformatics in expediting diagnostic testing and improving patient safety in healthcare.
References
- S. V. Pillai and R. S. Kumar, "The Role of Data-Driven Artificial Intelligence on COVID-19 Disease Management in Public Sphere: A Review," Decision, vol. 48, pp. 375-389, 2021. [Online]. Available: https://doi.org/10.1007/s40622-021-00289-3.
- M. K. Pandit, S. A. Banday, R. Naaz, and M. A. Chishti, "Automatic detection of COVID-19 from chest radiographs using deep learning," Radiography, vol. 27, no. 2, pp. 483-489, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1078817420302285.
- J. Wang, V. Chavda, R. Prajapati, A. Bedse, J. Patel, S. Popat, et al., "An Amalgamation of Bioinformatics and Artificial Intelligence for COVID-19 Management: From Discovery to Clinic," Current Research in Biotechnology, p. 100159, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2590262823000412.
- A. Rabaan et al., "Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer," Cancers, vol. 14, no. 22, p. 5595, 2022. [Online]. Available: https://doi.org/10.3390/cancers14225595.
- R. Deepa, S. Arunkumar, V. Jayaraj, and A. Sivasamy, "Healthcare’s new Frontier: AI-driven early cancer detection for improved well-being," AIP Advances, vol. 13, no. 11. [Online]. Available: https://doi.org/10.1063/5.0177640.
- M. Elhadary et al., "Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning," Blood Reviews, p. 101134, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0268960X23000954.
- D. Shao et al., "Artificial intelligence in clinical research of cancers," Briefings in Bioinformatics, vol. 23, no. 1, p. bbab523, 2022. [Online]. Available:
https://academic.oup.com/bib/article/23/1/bbab523/6470966.
- T. R. Undru et al., "Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis - a Review," Maedica, vol. 17, no. 2, pp. 420-426, 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375890/.
- L. P. Zhuhadar and M. D. Lytras, "The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions," Sustainability, vol. 15, no. 18, p. 13484, 2023. [Online]. Available: https://doi.org/10.3390/su151813484.
- E. Kogan et al., "A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records," International Journal of Cardiology, vol. 374, pp. 95-99, 2023. [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S0167527322018903.
- H. Alshazly, C. Linse, E. Barth, and T. Martinetz, "Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning" Sensors, vol. 21, no. 2, p. 455, Jan. 2021. [Online]. Available: https://doi.org/10.3390/s21020455.
- A.K. Yadav, D. Verma, A. Kumar, et al., "The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence, and the Internet of Medical Things toward COVID-19 diagnosis and management," Materials Today Chemistry, vol. 20, p. 100443, June 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2468519421000239
- M. Feleke, J. Cunningham, J. B. Parr, et al., "Machine learning guided aptamer refinement and discovery," Nature Communications, vol. 12, p. 2366, 2021. [Online]. Available: https://www.nature.com/articles/s41467-021-22555-9
- D. A. Hossain, L. M. Sedger, and J. Canning, "A Cross-Disciplinary View of Testing and Bioinformatic Analysis of SARS-CoV-2 and Other Human Respiratory Viruses in Pandemic Settings," IEEE Access, vol. 9, pp. 2681-2690, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9638682.
- H. Kumar and S. Sharma, "Contribution of Deep Learning in Bioinformatics," Global Journal on Application of Data Science and Internet of Things, vol. 5, no. 1, pp. 15, 2021. [Online]. Available: (HTTP.NET). ISSN: 2581-4370. Eureka Journals 2021. All Rights Reserved.
- F. M. De La Vega, S. Chowdhury, B. Moore, et al., "Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases," Genome Medicine, vol. 13, no. 153, pp. 1-14, 2021. [Online]. Available: https://link.springer.com/article/10.1186/s13073-021-00965-0.
- K. Pritzker, "Impact of the COVID-19 pandemic on Molecular Diagnostics," Journal of Molecular Diagnostics, vol. 25, no. 3, pp. 123-145, 2021. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/33910436/.
- T. Behl, I. Kaur, A. Sehgal, S. Singh, S. Bhatia, et al., "Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry," International Journal of Molecular Sciences, vol. 22, no. 12, p. 6184, 2021. [Online]. Available: https://www.mdpi.com/1422-0067/22/12/6184.
- D. W. Bates, D. Levine, A. Syrowatka, M. Kuznetsova, K. J. T. Craig, A. Rui, G. P. Jackson, and K. Rhee, "The potential of artificial intelligence to improve patient safety: a scoping review," Nature Partner Journals Digital Medicine, vol. 4, p. 41, 2021. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/33742085/.
- A. Mohammed, "An in-depth analysis of the use of artificial intelligence in the search for a cure for COVID-19 and the development of vaccines," International Journal of Novel Research in Engineering and Science, ISSN: 2456-4184, 2021. [Online]. Available: https://www.ijnrd.org/viewpaperforall?paper=IJNRD2107001.
- H. Louati, A. Louati, R. Lahyani, E. Kariri, and A. Albanyan, "Advancing Sustainable COVID-19 Diagnosis: Integrating Artificial Intelligence with Bioinformatics in Chest X-ray Analysis," Information, vol. 15, no. 4, p. 189, 2024. [Online]. Available: https://www.mdpi.com/2078-2489/15/4/189
- Ijaz, M. Nabeel, U. Masood, T. Mahmood, M. S. Hashmi, I. Posokhova, et al., "Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey," Informatics in Medicine Unlocked, vol. 29, p. 100832, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S235291482100294X.
- S. B. Khanagar, L. Alkadi, M. A. Alghilan, S. Kalagi, M. Awawdeh, L. K. Bijai, et al., "Application and performance of artificial intelligence (AI) in oral cancer diagnosis and prediction using histopathological images: a systematic review," Biomedicines, vol. 11, no. 6, p. 1612, 2023. [Online]. Available: https://www.mdpi.com/2227-9059/11/6/1612.
- P. N. Srinivasu, J. Shafi, T. B. Krishna, C. N. Sujatha, S. P. Praveen, and M. F. Ijaz, "Using recurrent neural networks for predicting type-2 diabetes from genomic and tabular data," Diagnostics, vol. 12, no. 12, p. 3067, 2022. [Online]. Available: https://www.mdpi.com/2075-4418/12/12/3067.
- K. Kim, J. W. Choi, Z. Jiao, D. Wang, J. Wu, T. Y. Yi, et al., "An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data," NPJ Digital Medicine, vol. 5, no. 1, p. 5, 2022. [Online]. Available:
https://www.nature.com/articles/s41746-021-00546-w.
- J. Mata Calidonio and K. Hamad-Schifferli, "An approach to use machine learning to optimize paper immunoassays for SARS-CoV-2 IgG and IgM antibodies," Nanoscale, vol. 3, pp. 677-687, 2024. [Online]. Available: https://pubs.rsc.org/en/content/articlehtml/2024/sd/d3sd00327b.
- K. Guo, X. Fu, H. Zhang, M. Wang, S. Hong, and S. Ma, "Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data," Translational Pediatrics, vol. 10, no. 1, pp. 33-43, Jan. 2021. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882284/.
- M. Zhao, J. M. Havrilla, L. Fang, Y. Chen, J. Peng, C. Liu, C. Wu, M. Sarmady, P. Botas, and J. Isla, "Phen2Gene: Rapid phenotype-driven gene prioritization for rare diseases," NAR Genomics and Bioinformatics, vol. 2, no. 2, p. lqaa032, June 2020. [Online]. Available: https://doi.org/10.1093/nargab/lqaa032.
- M. (Behdad) Jamshidi, O. Moztarzadeh, A. Jamshidi, A. Abdelgawad, A. S. El-Baz, and L. Hauer, "Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning," Future Internet, vol. 15, no. 4, p. 142, 2023. [Online]. Available: https://doi.org/10.3390/fi15040142.
- L. Shu, T. Maroilley, and M. Tarailo-Graovac, "The Power of Clinical Diagnosis for Deciphering Complex Genetic Mechanisms in Rare Diseases," Genes, vol. 14, no. 1, p. 196, 2023. [Online]. Available: https://doi.org/10.3390/genes14010196.
