The recent advancements in biotechnology and Artificial Intelligence (AI) have brought about an industry-transforming convergence with a huge potential to disrupt the healthcare sector. This review paper presents recent case studies on applications, benefits, and challenges in the healthcare industry by looking at the Biotechnology-AI Nexus. This article aims to create some background for further research, supplying information on the contemporary state of AI and biotechnology. Examples of areas covered in the study include drug development, genetics, proteomics, personalized medicine, and medical imaging. In addition, the latest breakthroughs and treatment techniques emerged from the fusion of AI with biotechnological methods such as CRISPR-Cas9 and gene-editing tools. The biotechnology artificial intelligence nexus has several applications. AI-enabled biotechnological breakthroughs may optimize workflow, make healthcare systems more efficient, and save costs in places that are not most needed. Furthermore, AI-powered analytics may illuminate complicated biological processes, creating data-centric choices that increase accuracy and personalization in healthcare. On the other hand, some impediments must be addressed before the Biotechnology-AI Nexus's actualization. Data privacy and security, ethical considerations, regulatory compliance, and cross-cutting teamwork are some of the issues that should be addressed. The possibility for AI and automation to disrupt the employment market also raises concerns regarding the displacement of workers and the need for re-skilling and up-skilling programs.

Keywords: Biotechnology, Artificial intelligence, Nexus, Health, Diagnosis, Biological, Drug, Cancer, Machine learning, Advancements.

Abonamah, A.A., Tariq, M.U., & Shilbayeh, S. (2021). On the Commoditization of Artificial Intelligence. Frontiers in Psychology, 12: 696346. https://doi.org/10.3389/fpsyg.2021.696346.

Aguilera, S., Quintana, L., Khan, T., Garcia, R., Shoman, H., Caddell, L., & Dempsey, R. (2020). Global health, global surgery and mass casualties: II. Mass casualty centre resources, equipment and implementation. BMJ Global Health, 5(1): e001945. https://doi.org/10.1136/bmjgh-2019-001945.

Ahmed, M.N., Toor, A.S., O'Neil, K., & Friedland, D. (2017). Cognitive computing and the future of health care cognitive computing and the future of healthcare: the cognitive power of IBM Watson has the potential to transform global personalized medicine. IEEE Pulse, 8(3): 4–9. https:doi.org/10.1109/MPUL.2017.2678098.

Al-Alusi, M.A., Ding, E., McManus, D.D., & Lubitz, S.A. (2019). Wearing your heart on your sleeve: the future of cardiac rhythm monitoring. Current Cardiology Reports, 21: 1–11.

Arora, S., et al. (2022). Biotechnological Innovations for Environmental Bioremediation. Springer Nature.

Asia, A.O., Zhu, C.Z., Althubiti, S.A., Al-Alimi, D., Xiao, Y.L., Ouyang, P.B., & Al-Qaness, M.A. (2022). Detection of diabetic retinopathy in retinal fundus images using CNN classification models. Electronics, 11(17): 2740. https://doi.org/10.3390/electronics11172740.

Benjamin, E.J., Blaha, M.J., Chiuve, S.E., Cushman, M., Das, S.R., Deo, R., & Gillespie, C. (2017). Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation, 135(10): e146–e603.  https://doi.org/10.1161/CIR.0000000000000485.

Blasimme, A., & Vayena, E. (2019). The ethics of AI in biomedical research, patient care and public health. Patient Care and Public Health (April 9, 2019). Oxford Handbook of Ethics of Artificial Intelligence, Forthcoming. https://doi.org/10.1093/oxfordhb/9780190067397.013.45.

Chen, C., Yaari, Z., Apfelbaum, E., Grodzinski, P., Shamay, Y., & Heller, D.A. (2022). Merging data curation and machine learning to improve nanomedicines. Advanced Drug Delivery Reviews, 114172. https://doi.org/10.10 16/j.addr.2022.114172.

Chen, J.H., & Asch, S.M. (2017). Machine learning and prediction in medicine—beyond the peak of inflated expectations. The New England Journal of Medicine, 376(26): 2507. https://doi.org/10.1056/NEJMp1702071.

Dash, S., Shakyawar, S.K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1): 1–25. https://doi.org/10.1186/s40537-019-0217-0.

Gerke, S., Yeung, S., & Cohen, I.G. (2020). Ethical and legal aspects of ambient intelligence in hospitals. Jama, 323(7): 601–602.  https://doi.org/10.1001/jama.2019.21699.

Goldenthal, I.L., Sciacca, R.R., Riga, T., Bakken, S., Baumeister, M., Biviano, A.B., & Whang, W. (2019). Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results. Journal of Cardiovascular Electrophysiology, 30(11): 2220–2228.  

Gorjian, S., Sharon, H., Ebadi, H., Kant, K., Scavo, F.B., & Tina, G.M. (2021). Recent technical advancements, economics and environmental impacts of floating photovoltaic solar energy conversion systems. Journal of Cleaner Production, 278: 124285. https://doi.org/10.1016/j.jclepro.2020.124285.

Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., & Cuadros, J. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22): 2402–2410.  https://doi.org/10.1001/jama.2016.17216.

Haidari, L.A., Brown, S.T., Ferguson, M., Bancroft, E., Spiker, M., Wilcox, A., & Lee, B.Y. (2016). The economic and operational value of using drones to transport vaccines. Vaccine, 34(34): 4062–4067. 

Hassanzadeh, P., Atyabi, F., & Dinarvand, R. (2019). The significance of artificial intelligence in drug delivery system design. Advanced Drug Delivery Reviews, 151: 169–190. https://doi.org/10.1016/j.addr.2019.05.001.

Hesham, A.E.L., Kaur, T., Devi, R., Kour, D., Prasad, S., Yadav, N., & Yadav, A.N. (2021). Current trends in microbial biotechnology for agricultural sustainability: conclusion and future challenges. Current Trends in Microbial Biotechnology for Sustainable Agriculture, Pages 555–572. Doi: 10.1007/978-981-15-6949-4_22.

Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023). AI for life: Trends in artificial intelligence for biotechnology. New Biotechnology, 74: 16–24.

Holzinger, A., Weippl, E., Tjoa, A.M., & Kieseberg, P. (2021). Digital transformation for sustainable development goals (sdgs)-a security, safety and privacy perspective on AI. Paper presented at the Machine Learning and Knowledge Extraction: 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, Virtual Event, August 17–20, 2021, Proceedings 5.

Johnson, K.B., Wei, W.Q., Weeraratne, D., Frisse, M.E., Misulis, K., Rhee, K., & Snowdon, J.L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1): 86–93. https:// doi.org/10.1111/cts.12884.

Kashkha, A. (2023). The future of artificial intelligence in biotechnology: Current challenges and outcomes. Medium. 

Kaswan, K.S., Dhatterwal, J.S., Kumar, N., & Lal, S. (2023). Artificial Intelligence for Financial Services. In Contemporary Studies of Risks in Emerging Technology, Part A., Pages 71–92, Emerald Publishing Limited. https://doi.org/10.1108/978-1-80455-562-020231006.

Kim, B. (2020). Moving forward with digital disruption: What big data, IoT, synthetic biology, AI, blockchain, and platform businesses mean to libraries. https://doi.org/10.5860/ltr.56n2.

Kropp, M., Golubnitschaja, O., Mazurakova, A., Koklesova, L., Sargheini, N., Vo, T.T.K.S., & Polivka, J. (2023). Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications—Risks and mitigation. EPMA Journal, 14(1): 21–42. https://doi.org/10.1007/s13167-023-00314-8.

Kuddus, K. (2022). Artificial Intelligence in Language Learning: Practices and Prospects. Advanced Analytics and Deep Learning Models, Pages 1–17.  https://doi.org/10.1002/9781119792437.ch1.

Lockhart, A., While, A., Marvin, S., Kovacic, M., Odendaal, N., & Alexander, C. (2021). Making space for drones: The contested reregulation of airspace in Tanzania and Rwanda. Transactions of the Institute of British Geographers, 46(4): 850–865. https://doi.org/10.1111/tran.12448.

Lysunets, T. (n.d.). Biotechnology» and «Technosphere Safety».

Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956): 259–265. 

Padhy, I., Mahapatra, A., Saraswat, R., & Song, J. (2020). Role of biotechnology in pharmaceutical research: A comprehensive review. Pharm Sci., 7: 472–486.

Prado, D.A., Acosta-Acero, M., & Maldonado, R.S. (2020). Gene therapy beyond luxturna: a new horizon of the treatment for inherited retinal disease. Current Opinion in Ophthalmology, 31(3): 147–154.  

Raman, R., Srinivasan, S., Virmani, S., Sivaprasad, S., Rao, C., & Rajalakshmi, R. (2019). Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye, 33(1): 97–109. https://doi.org/ 10.1038/s41433-018-0269-y.

Rebelo, N., Sanders, L., Li, K., & Chow, J.C. (2022). Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence–Assisted Chatbot: Development Study. JMIR Formative Research, 6(12): e39443. https:// doi.org/10.2196/39443.

Rodrigues, A.G. (2020). Global players: resources and profits. In New and Future Developments in Microbial Biotechnology and Bioengineering, Pages 187–208, Elsevier. doi: 10.1016/B978-0-444-64301-8.00009-3.

Schaeffer, D.M., & Olson, P.C. (2019). Drones: 4DT Applications in US Industry and Public Policy. Journal of Strategic Innovation and Sustainability, 14(3): 93–97.

Shava, E. (2022). Survival of african governments in the fourth industrial revolution. Africa and the Fourth Industrial Revolution: Curse or Cure?, Pages 125–144.

Singh, P., Singh, N., Singh, K.K., & Singh, A. (2021). Diagnosing of disease using machine learning. In Machine learning and the internet of medical things in healthcare, Pages 89–111, Elsevier. 

Somashekhar, S., Sepúlveda, M.J., Puglielli, S., Norden, A., Shortliffe, E.H., Kumar, C.R., & Rhee, K. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology, 29(2): 418–423. https://doi.org/10.1093/annonc/mdx781.

Steinhubl, S.R., Waalen, J., Edwards, A.M., Ariniello, L.M., Mehta, R.R., Ebner, G.S., & Sarich, T. (2018). Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial. Jama, 320(2): 146–155. http://doi.org/10.1001/jama.2018.8102.

Tarr, A.A., Perera, A.G., Chahl, J., Chell, C., Ogunwa, T., & Paynter, K. (2021). Drones—healthcare, humanitarian efforts and recreational use. In Drone Law and Policy, Pages 35–54, Routledge.

Ting, D.S.W., Pasquale, L.R., Peng, L., Campbell, J.P., Lee, A.Y., Raman, R., & Wong, T.Y. (2019). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103(2): 167–175. https://doi. org/10.1136/bjophthalmol-2018-313173.

Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1): 44–56. https://doi.org/10.1038/s41591-018-0300-7.

Truong, Y., & Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review and future research agenda. Pages 121852, Elsevier. https://doi.org/10.1016/j.techfore.2022.121852.

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., & Spitzer, M. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6): 463–477. https:// doi.org/10.1038/s41573-019-0024-5.

Veale, E.L., Stewart, A.J., Mathie, A., Lall, S.K., Rees-Roberts, M., Savickas, V., & Corlett, S.A. (2018). Pharmacists detecting atrial fibrillation (PDAF) in primary care during the influenza vaccination season: a multisite, cross-sectional screening protocol. BMJ Open, 8(3): e021121. doi: 10.1136/bmjopen-2017-021121.

Velidandi, A., Gandam, P.K., Chinta, M.L., Konakanchi, S., Bhavanam, A.R., Baadhe, R.R., & Gupta, V.K. (2023). State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery. Journal of Energy Chemistry. https://doi.org/10.1016/j.jechem.2023.02.020.

Vijaya, G. (2022). Deep Learning-Based Computer-Aided Diagnosis System. In Application of Deep Learning Methods in Healthcare and Medical Science, Pages 23–48, Apple Academic Press.

Waden, J. (2022). Artificial intelligence and its role in the development of personalized medicine and drug control: artificial intelligence and its role in the development of personalized medicine and drug control. Wasit Journal of Computer and Mathematics Sciences, 1(4): 194–206. https://doi.org/10.31185/wjcm.85.

Wan, S., Gu, Z., & Ni, Q. (2020). Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 149: 99–106. https://doi.org/10.1016/j.comcom.2019.10.012.

Yathiraju, N., & Mohapatra, A. (2023). The Implications of IoT in the Modern Healthcare Industry post COVID-19. https://doi.org/10.3389/fpsyg.2021.696346.

Source of Funding:

This review was conducted without external funding. The authors would like to clarify that no financial support or grants from any organization, institution, or individual were received for the completion of this review. The research was carried out as part of the authors' academic and professional responsibilities, and they did not rely on any external sources of funding for the design, execution, or publication of this work.

Conflict of Interest:

This research was conducted in the absence of any commercial or financial relationships that could be construed as potential absence of conflict of interest.

Consent for Publication:

The authors declare that they consented to the publication of this study.

Authors’ Contribution:

All authors of this review article, have worked collaboratively and contributed equally to every aspect of this study. From conceptualization to writing, review, and finalization, each author has played an equal and vital role in the development of this manuscript. The collaborative effort and shared responsibilities among all authors have ensured a balanced and comprehensive approach to this research. All authors have collectively read and endorsed the final version of the manuscript, highlighting their equal and substantial contributions to the research and publication process.