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Cutting-Edge Applications of Artificial Intelligence in Healthcare, Petroleum Fraud Detection, and Innovative Strategies in Cancer Treatment

Authors

  • George Edison Independent Researcher France

DOI:

10.47709/ijmdsa.v3i4.4892

Keywords:

big data, ethical considerations, algorithmic bias, operational efficiency, personalized medicine, fraud detection, telemedicine, artificial intelligence, healthcare, petroleum industry, cancer diagnostics, and multidisciplinary cooperation

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Abstract

Artificial intelligence (AI) integration in the petroleum and healthcare sectors is quickly changing operating procedures and improving results in both domains. This overview examines the various applications of AI, including fraud detection, tailored medicine, and cancer diagnosis. By evaluating enormous datasets from several sources, such as genetic and electronic health records, artificial intelligence (AI) tools in the healthcare industry greatly increase diagnostic accuracy, enable early diagnosis, and customize treatment plans. AI-supported telemedicine and remote monitoring improve patient access to care and streamline healthcare delivery. By forecasting equipment failures, detecting fraudulent activity, and improving drilling operations, artificial intelligence (AI) improves operational efficiency in the petroleum industry, lowering costs and increasing safety. Notwithstanding these developments, there are still several obstacles to the successful application of AI, including issues with data quality, algorithmic bias, regulatory compliance, and ethical considerations. Strong data management procedures, openness in AI decision-making, and interdisciplinary cooperation between technologists, medical practitioners, and industrial stakeholders are all necessary to meet these issues. With possible advancements in big data integration, AI-driven telemedicine, and predictive analytics, the future of AI in various fields is marked by ongoing innovation. The entire potential of AI technology will eventually be unlocked by adopting responsible AI practices and encouraging teamwork, which will result in better patient outcomes, more operational integrity, and sustainable growth in a landscape that is becoming more complicated by the day.

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ARTICLE Published HISTORY

Submitted Date: 2024-10-31
Accepted Date: 2024-10-31
Published Date: 2024-10-31