Artificial intelligence (AI) is reshaping infectious disease diagnostics by supporting clinical decision making, optimising laboratory and clinical workflows, and enabling real-time disease surveillance. AI approaches improve pathogen detection, antimicrobial stewardship, and treatment monitoring, enhancing diagnostic accuracy, efficiency, and scalability. The role of AI in combating antimicrobial resistance is particularly significant, enabling rapid pathogen identification and personalised treatment. Despite progress over the past two decades, widespread AI adoption in infectious disease diagnostics faces challenges. In high-income countries, fragmented data ecosystems, incomplete datasets, and algorithmic bias hinder clinical integration. Meanwhile, low-income and middle-income countries contend with limited digital infrastructure, unstandardised data, and financial constraints, exacerbating disparities in diagnostic access. Further barriers include concerns over interoperability, data privacy, cybersecurity, and the regulation of AI implementation. This paper examines the role of AI in infectious disease diagnostics, highlighting both opportunities and limitations. It underscores the need for coordinated investments in digital infrastructure, harmonised data-sharing frameworks, and clinician engagement to support equitable, sustainable adoption. Addressing these challenges will enable health-care systems to harness the potential of AI to improve infectious disease detection, prevention, and management of infectious diseases, thereby strengthening global health resilience.