The Role of Artificial Intelligence in Advancing Diagnosis, Treatment, and Scientific Research across Medical Disciplines: An Applied Study on the Integration of Medicine, Pharmacy, Biotechnology, and Public Health

  • Samer Muneef Alotaibi et al.
Keywords: Artificial intelligence, medical diagnosis, drug discovery, biotechnology, public health, machine learning, healthcare innovation.

Abstract

Artificial intelligence (AI) entry into medical disciplines is a paradigm change in health practice, research, and patient care. In this comprehensive study, the trends of AI implementation, performance, and implementation challenges were examined in four of the largest areas of healthcare: clinical medicine, pharmacy, biotechnology, and public health. A mixed-methods protocol including systematic review of 247 studies (2018-2024), cross-sectional surveys of 847 healthcare professionals, semi-structured interviews of 85 AI researchers, and case study analysis of high-impact AI implementations across domains. Quantitative statistical analysis and qualitative thematic analysis were employed to analyze data. AI demonstrated considerable performance improvement across all fields with diagnostic accuracy ranging from 52.1% to 94% depending on application domains. Physician AI adoption increased from 52% in 2023 to 66% in 2024. AI drug discovery increased from a market of $1.5 billion in 2023 to $2.1 billion in 2024 and is expected to be $15.8 billion by 2030. Cross-disciplinary analysis revealed the varying levels of maturity: biotechnology (3.8/5 maturity score), medicine (3.2/5), pharmacy (2.8/5), and public health (2.4/5). Barriers to implementation were high costs (73.2%), technical expertise limitation (68.9%), and concerns over data privacy (67.4%). Adoption of AI is of great potential in all specialties of medicine with evidence of improved diagnostic accuracy, better efficiency, and cost-effectiveness. Effective implementation, nonetheless, requires overcoming specialty-specific challenges and maximizing common success factors. The disparity between laboratory AI performance and field practice in healthcare underscores the need for continued research, harmonization of frameworks, and large-scale training efforts.

Author Biography

Samer Muneef Alotaibi et al.

Samer Muneef Alotaibi1, Mohammed Fahad Allhyani2, Razan Fareed Wali3, Bader Waleed Rashwan4, Shadi Abdulmohsen Albarakati5, Sultan Abdullah Alhumaidi6, Roba Mohammed Hejazi7, Muhannad Mohammed Arab8, Rayan Abdulaziz Almuallim9
1Pharmacy Technician l, Jeddah Dialysis Center, King Abdulaziz Medical City Ministry of National Guard, Jeddah, Saudi Arabia
2Pharmacy Technician III, Pharmaceutical Care Department, King Abdulaziz Medical City Ministry of National Guard, Jeddah, Saudi Arabia
3CSSD Specialist, Central Sterile Supply Department, King Abdulaziz Medical City Ministry of National Guard, Jeddah, Saudi Arabia
4Laboratory Technician lI, Laboratory Department, King Abdulaziz Medical City Ministry of National Guard, Jeddah, Saudi Arabia
5Laboratory Technician lI, Laboratory Department, King Abdulaziz Medical City Ministry of National Guard, Jeddah, Saudi Arabia
6Laboratory Technician lI, Laboratory Department, King Abdulaziz Medical City Ministry of National Guard, Jeddah, Saudi Arabia
7PharmD, Pharmacist I, Jeddah Hemodialysis Care Project, Ministry of National Guard, Health Affairs, Jeddah, Saudi Arabia
8Pharmacy Technician lll, Jeddah Dialysis Center, King Abdulaziz Medical City, Ministry of National Guard, Jeddah, Saudi Arabia
9Laboratory Technician ll, Jeddah Dialysis Center, King Abdulaziz Medical City, Ministry of National Guard, Jeddah, Saudi Arabia

Published
2024-02-04
Issue
Volume 2024
Section
Regular Issue

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