Daniel Amoah-Oppong is a Ghanaian physical educator, researcher, and teacher pursuing a PhD in Physical Education (Curriculum and Pedagogy) at the University of Cape Coast, Ghana. His research interests include inclusive physical education, sport psychology, curriculum and pedagogy, referee development, and artificial intelligence in education. He has published widely in peer-reviewed journals, presented at international conferences, and received the Best Paper Presentation Award at the 2026 International Seminar on Empowering Young Minds for a Sustainable Future. He is committed to advancing quality education, sports development, and evidence-based research.

Artificial Intelligence and Bioinformatics for Precision Health: Ethical, Computational, and Translational Frontiers in Contemporary Biomedical Science   Abstract: The rapid advancement of artificial intelligence (AI) is redefining bioinformatics and computational biology by expanding the capacity of researchers to interpret complex biological data, model disease mechanisms, and advance precision health. Contemporary biomedical science increasingly depends on computational systems that integrate genomic, proteomic, clinical, and population-level datasets into actionable biological and therapeutic insight. This study examines AI as both a methodological innovation and an ethical imperative within bioinformatics. It argues that the future of computational biology depends on the responsible alignment of algorithmic intelligence, biological validity, clinical relevance, reproducibility, and equitable data governance. This study employs a conceptual and analytical review design. It synthesises current developments in machine learning, deep learning, bioinformatics pipelines, computational modelling, and precision health research. The analysis is organised around four interrelated domains. This includes genomic and proteomic data interpretation, disease prediction and biomarker discovery, drug-target identification, and ethical governance in AI-assisted biomedical research. Particular attention is given to model transparency, data quality, dataset inclusivity, interdisciplinary collaboration, reproducibility, and the translation of computational outputs into clinically meaningful knowledge. The analysis establishes that AI significantly strengthens bioinformatics by improving the scale, speed, accuracy, and interpretive depth of biological data analysis. Machine learning and deep learning models support disease classification, gene expression profiling, protein structure prediction, drug discovery, risk prediction, and personalised therapeutic decision-making. The review also identifies major constraints that affect the scientific credibility and translational value of AI-assisted bioinformatics. These include inconsistent data quality, algorithmic opacity, population bias in training datasets, weak reproducibility, fragmented data infrastructures, limited clinical interpretability, and uneven access to computational resources. These findings demonstrate that AI in bioinformatics requires rigorous validation, ethical accountability, biological contextualisation, and responsible implementation across diverse biomedical settings. AI offers a transformative pathway for bioinformatics and computational biology, particularly in precision health, translational medicine, and global biomedical innovation. Its scientific value depends on the ability of researchers to design systems that are technically robust, biologically meaningful, clinically interpretable, and ethically governed. This study concludes that responsible AI must advance prediction, explanation, equity, and clinical usefulness. Bioinformatics can generate stronger disease models, accelerate therapeutic discovery, improve biomedical decision-making, and support more inclusive futures for precision health by integrating computational intelligence with biological reasoning and ethical governance. Keywords: Artificial Intelligence; Bioinformatics; Computational Biology; Precision Health; Genomics; Machine Learning; Biomedical Ethics; Translational Medicine