Evolution of Artificial Intelligence in Healthcare: From Historical Milestones to Current Applications and Future Prospects in Hospital and Pharmaceutical Innovations

Authors

  • Precious Esong Sone College of Business, Department of Healthcare Management, East Carolina University, Greenville, NC, USA https://orcid.org/0009-0006-0211-0053
  • Prof Victor Mbarika College of Business, East Carolina University, Greenville, NC, USA
  • Joy Nsang Taboh College of Arts and Sciences, Department of Biology, East Carolina University, Greenville, NC, USA
  • Majoie Mendouga Ngandi College of Engineering, Department of Computer Science, East Carolina University, Greenville, NC, USA https://orcid.org/0009-0007-2167-6519
  • Ryan Metuge Balungeli College of Engineering, Department of Computer Science, East Carolina University, Greenville, NC, USA
  • Ayomide Samuel Ogunrinde College of Political Science, Department of Public Administration, East Carolina University, Greenville, NC, USA https://orcid.org/0009-0001-9194-0828
  • Kalyana Krishna Kondapalli Technical Manager, Wallstreet Consulting Services, NC, USA https://orcid.org/0009-0003-0832-259X
  • Nissi Ekole Balungeli College of Engineering, Department of Information Technology and Cybersecurity, East Carolina University, Greenville, NC, USA https://orcid.org/0009-0004-1171-0371

Keywords:

Artificial Intelligence, Healthcare, Drug Discovery, Personalized Medicine, Ethical Considerations

Abstract

Artificial Intelligence (AI) is reshaping healthcare by advancing diagnostics, treatment planning, drug discovery, and operational efficiency. Since its introduction in the 1950s, AI has progressed from early systems like MEDLARS, MYCIN, and INTERNIST-I to deep learning tools capable of specialist-level performance in medical imaging and predictive analytics. This paper traces AI’s evolution in healthcare, emphasizing historical milestones, current applications, and emerging directions. In the pharmaceutical domain, AI expedites drug discovery, enhances clinical trial efficiency, and personalizes treatment strategies. It also improves hospital workflows, patient adherence, and supply chain management. However, challenges persist, including data privacy, algorithmic transparency, and ethical concerns. Future efforts focus on interpretable AI models, robust data integration, and ethical frameworks. The integration of AI with technologies like blockchain and IoT holds promise for a more personalized, efficient, and accessible healthcare system.

Downloads

Published

2024-06-30

Issue

Vol. 1 No. 01 (2024): BioMedPha

Section

Articles