Revolutionizing Healthcare with AI: Integrating Petroleum Insights, Herbal Medicine, and Fraud Detection through ChatGPT-Enhanced Solutions

Authors

  • Ali Husnain Chicago State University
  • Muhammad Umer Qayyum Washington University of Science and Technology, Alexandria Virginia
  • Muhammad Fahad Washington University of Science and Technology, Alexandria Virginia
  • Muhammad Ibrar New Mexico highlands university Las Vegas, NM

Abstract

Artificial Intelligence (AI) is changing the healthcare and fraud detection industries by improving results, efficiency, and accuracy. A number of important sectors where AI has had a substantial impact are highlighted in this thorough investigation, which is backed up with noteworthy case studies. Artificial intelligence (AI) technologies have completely changed the way that fraudulent operations are recognized and stopped. For instance, the Medicare Fraud Prevention System (FPS) analyzes claims data using machine learning, which dramatically lowers fraudulent payments and enhances program integrity. Similar to this, real-time transaction data analysis using AI has significantly decreased financial losses in credit card fraud protection systems. AI platforms that show off the revolutionary potential of AI in clinical contexts are IBM Watson for Oncology and PathAI. While PathAI helps pathologists analyze medical pictures and identify abnormalities more precisely, Watson for Oncology supports oncologists by offering evidence-based therapy suggestions and improving diagnostic accuracy. These apps expedite the diagnosis procedure and enhance patient care. The incorporation of petroleum sector knowledge into AI models for healthcare highlights the importance of interdisciplinary innovation. Healthcare benefits from the use of petroleum industry advances in data analytics, material science, and operational optimization, which result in better patient care, more efficient operations, and creative solutions. Notwithstanding these developments, issues with data privacy, accuracy, integration, and ethics still need to be addressed. To fully utilize AI, these problems must be resolved by ongoing validation, expert cooperation, and adherence to legal requirements. All things considered, the continued development of AI technologies indicates more breakthroughs in many fields. We can build more safe, effective, and efficient systems that help people and businesses by embracing AI and addressing related issues.

References

N. Cunningham. The 10 worst energy-related disasters of modern times. https://oilprice.com/Energy/Coal/ Coal-The-Worlds-Deadliest-Source-Of-Energy.html, last accessed on 08/10/20

N. E. Institution. Chernobyl accident and its consequences. https://www.nei.org/resources/factsheets/chernobyl-accident-and-its-consequences, last accessed on 04/03/21

S. Institute. Confirmation of a coordinated attack on the Ukrainian power grid. https://www.sans.org/blog/ confirmation-of-a-coordinated-attack-on-the-ukrainian-power-grid/, last accessed on 01/01/21.

N. E. Services. Energy theft and fraud reduction. Https: //www.smart-energy.com/industry-sectors/energy-grid-management/ energy-theft-and-fraud-reduction/, last accessed on 02/11/21

Husnain, A., Alomari, G., & Saeed, A. AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.

M. V. Barros, R. Salvador, C. M. Piekarski, A. C. de Francisco, and F. M. C. S. Freire, “Life cycle assessment of electricity generation: a review of the characteristics of existing literature,” The International Journal of Life Cycle Assessment, vol. 25, no. 1, pp. 36–54, 2020.

B. L. Lee, C. Wilson, P. Simshauser, and E. Majiwa, “Deregulation, efficiency and policy determination: An analysis of australia’s electricity distribution sector,” Energy Economics, p. 105210, 2021.

Zeb, S., Nizamullah, F. N. U., Abbasi, N., & Fahad, M. (2024). AI in Healthcare: Revolutionizing Diagnosis and Therapy. International Journal of Multidisciplinary Sciences and Arts, 3(3).

J. D. Hunt, E. Byers, Y. Wada, S. Parkinson, D. E. Gernaat, S. Langan, D. P. van Vuuren, and K. Riahi, “Global resource potential of seasonal pumped hydropower storage for energy and water storage,” Nature communications, vol. 11, no. 1, pp. 1–8, 2020

Zeb, S., Nizamullah, F. N. U., Abbasi, N., & Qayyum, M. U. (2024). Transforming Healthcare: Artificial Intelligence's Place in Contemporary Medicine. BULLET: Jurnal Multidisiplin Ilmu, 3(4).

T. Simla and W. Stanek, “Reducing the impact of wind farms on the electric power system by the use of energy storage,” Renewable Energy, vol. 145, pp. 772–782, 2020

HUSNAIN, A., & SAEED, A. (2024). AI-Enhanced Depression Detection and Therapy: Analyzing the VPSYC System.

Abbasi, N., Nizamullah, F. N. U., & Zeb, S. (2023). AI in Healthcare: Integrating Advanced Technologies with Traditional Practices for Enhanced Patient Care. BULLET: Jurnal Multidisiplin Ilmu, 2(3), 546-556.

F. Leach, G. Kalghatgi, R. Stone, and P. Miles, “The scope for improving the efficiency and environmental impact of internal combustion engines,” Transportation engineering, p. 100005, 2020.

Abbasi, N., Nizamullah, F. N. U., & Zeb, S. (2023). AI IN HEALTHCARE: USING CUTTING-EDGE TECHNOLOGIES TO REVOLUTIONIZE VACCINE DEVELOPMENT AND DISTRIBUTION. JURIHUM: Jurnal Inovasi dan Humaniora, 1(1), 17-29.

Lodhi, S. K., Gill, A. Y., & Hussain, H. K. (2024). Green Innovations: Artificial Intelligence and Sustainable Materials in Production. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 492-507.

Lashari, Z. A., Lalji, S. M., Ali, S. I., Kumar, D., Khan, B., & Tunio, U. (2024). Physiochemical analysis of titanium dioxide and polyacrylamide nanofluid for enhanced oil recovery at low salinity. Chemical Papers, 78(6), 3629-3637.

Okulicz-Kozaryn and M. Altman, “The happiness-energy paradox: Energy use is unrelated to subjective well-being,” Applied Research in Quality of Life, vol. 15, no. 4, pp. 1055–1067, 2020.

Samad, A., & Jamal, A. (2024). Transformative Applications of ChatGPT: A Comprehensive Review of Its Impact across Industries. Global Journal of Multidisciplinary Sciences and Arts, 1(1), 26-48.

L. Cheng and T. Yu, “A new generation of ai: A review and perspective on machine learning technologies applied to smart energy and electric power systems,” International Journal of Energy Research, vol. 43, no. 6, pp. 1928–1973, 2019.

E. Mollasalehi, D. Wood, and Q. Sun, “Indicative fault diagnosis of wind turbine generator bearings using tower sound and vibration,” Energies, vol. 10, no. 11, p. 1853, 2017.

M. Akhloufi and N. Benmesbah, “Outdoor ice accretion estimation of wind turbine blades using computer vision,” in 2014 Canadian Conference on Computer and Robot Vision. IEEE, 2014, pp. 246–253

F. Miralles, N. Pouliot, and S. Montambault, “State-of-the-art review ` of computer vision for the management of power transmission lines,” in Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry. IEEE, 2014, pp. 1–6.

T. Azar, A. Khamis, N. A. Kamal, and B. Galli, “Short term electricity load forecasting through machine learning,” in Joint European-US Workshop on Applications of Invariance in Computer Vision. Springer, 2020, pp. 427–437.

Lodhi, S. K., Hussain, I., & Gill, A. Y. (2024). Artificial Intelligence: Pioneering the Future of Sustainable Cutting Tools in Smart Manufacturing. BIN: Bulletin of Informatics, 2(1), 147-162.

L. Du, J. Guo, and C. Wei, “Impact of information feedback on residential electricity demand in china,” Resources, Conservation and Recycling, vol. 125, pp. 324–334, 2017

P. Conde-Clemente, J. M. Alonso, and G. Trivino, “Toward automatic generation of linguistic advice for saving energy at home,” Soft Computing, vol. 22, no. 2, pp. 345–359, 2018.

Lodhi, S. K., Hussain, H. K., & Hussain, I. (2024). Using AI to Increase Heat Exchanger Efficiency: An Extensive Analysis of Innovations and Uses. International Journal of Multidisciplinary Sciences and Arts, 3(4), 1-14.

R. Jurowetzki, “Unpacking big systems–natural language processing meets network analysis. A study of smart grid development in denmark.” A Study of Smart Grid Development in Denmark. (May 21, 2015). SWPS, vol. 15, 2015.

Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis. International Journal of Multidisciplinary Sciences and Arts, 3(4), 15-25.

R. Jing, Y. Lin, N. Khanna, X. Chen, M. Wang, J. Liu, and J. Lin, “Balancing the energy trilemma in energy system planning of coastal cities,” Applied Energy, p. 116222, 2020

Valli, L. N. (2024). A succinct synopsis of predictive analytics for fraud detection and credit scoring in BFSI. JURIHUM: Jurnal Inovasi dan Humaniora, 2(2), 200-213.

S. Wang, D. Wang, Z. Yu, X. Dong, S. Liu, H. Cui, and B. Sun, “Advances in research on petroleum biodegradability in soil,” Environmental Science: Processes & Impacts, vol. 23, no. 1, pp. 9–27, 2021

Lashari, Z. A., Lalji, S. M., Ali, S. I., Kumar, D., Khan, B., & Tunio, U. (2024). Physiochemical analysis of titanium dioxide and polyacrylamide nanofluid for enhanced oil recovery at low salinity. Chemical Papers, 78(6), 3629-3637.

Z. U. ZANGO, “Review of petroleum sludge thermal treatment and utilization of ash as a construction material, a way to environmental sustainability,” International Journal of Advanced and Applied Sciences, vol. 7, no. 12, 2020.

World energy outlook 2017. https://www.iea.org/reports/ world-energy-outlook-2017, last accessed on 12/12/20.

Hussain, S. M. Arif, and M. Aslam, “Emerging renewable and sustainable energy technologies: State of the art,” Renewable and Sustainable Energy Reviews, vol. 71, pp. 12–28, 2017

Mehta, A., Niaz, M., Uzowuru, I. M., & Nwagwu, U. Implementation of the Latest Artificial Intelligence Technology Chatbot on Sustainable Supply Chain Performance on Project-Based Manufacturing Organization: A Parallel Mediation Model in the American Context.

S. Cao, Y. Chen, G. Cheng, F. Du, W. GAO, Z. He, S. Li, S. Lun, H. Ma, Q. Su et al., “Preliminary study on evaluation of smart-cities technologies and proposed uv lifestyles,” in 2018 4th International Conference on Universal Village (UV). IEEE, 2018, pp. 1–49

Valli, L. N. (2024). Predictive Analytics Applications for Risk Mitigation across Industries; A review. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 542-553.

Lodhi, S. K., Hussain, H. K., & Gill, A. Y. (2024). Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production. Global Journal of Universal Studies, 1(1), 108-131.

D. W. Kweku, O. Bismark, A. Maxwell, K. A. Desmond, K. B. Danso, E. A. Oti-Mensah, A. T. Quachie, and B. B. Adormaa, “Greenhouse effect: greenhouse gases and their impact on global warming,” Journal of Scientific research and reports, pp. 1–9, 2017

Choudhary, V., Mehta, A., Patel, K., Niaz, M., Panwala, M., & Nwagwu, U. (2024). Integrating Data Analytics and Decision Support Systems in Public Health Management. South Eastern European Journal of Public Health, 158-172.

Jamal, A. (2023). Embracing Nature's Therapeutic Potential: Herbal Medicine. International Journal of Multidisciplinary Sciences and Arts, 2(1), 117-126.

Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). 3D Printing Techniques: Transforming Manufacturing with Precision and Sustainability. International Journal of Multidisciplinary Sciences and Arts, 3(3), 129-138.

Underdal and K. Hanf, International environmental agreements and domestic politics: The case of acid rain. Routledge, 2019.

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Published

2024-10-04

How to Cite

Ali Husnain, Muhammad Umer Qayyum, Muhammad Fahad, & Muhammad Ibrar. (2024). Revolutionizing Healthcare with AI: Integrating Petroleum Insights, Herbal Medicine, and Fraud Detection through ChatGPT-Enhanced Solutions. BIN : Bulletin Of Informatics, 2(2), 262–275. Retrieved from https://ojs.jurnalmahasiswa.com/ojs/index.php/bin/article/view/358