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  1. Drug promotion and labeling in developing countries: an update
    Lee PR, Lurie P, Silverman MM, Lydecker M
    J Clin Epidemiol, 1991;44 Suppl 2:49S-55S.
    PMID: 2045842
    Recent studies of drug promotion and labeling in Third World countries since 1972 have observed important changes in the policies of multinational corporations. Earlier studies found that multinational and national drug companies often grossly exaggerated the indications for the drugs and minimized or ignored the hazards. In the latest study, initiated in 1987, considerable improvement in promotional practices of the multinational corporations has been found, but little or no improvement on the part of the national companies. As a result, physicians are still provided with grossly exaggerated claims and the hazards of prescription drugs are covered up or glossed over. A very serious problem--the marketing of fraudulent drug products--has been identified in a number of Third World countries. Drug products are shaped and colored to resemble the original multinational company product, but contain only a small percentage of the active ingredient stated on the label, or perhaps none at all. In Indonesia fraudulent drug products may represent 20-30% of all drug products in the market. Similar fraudulent products have been reported in Brazil, Thailand, Bangladesh and Malaysia.
  2. Fulltext Integrating AI into infection control: Evaluating the accuracy and consistency of four leading platforms across three regions
    Julkipli J, Alanazi A, Saw YT, Zainulabid UA, Delport J, Gupta R, et al.
    J Assoc Med Microbiol Infect Dis Can, 2026 Jun;11(2):133-140.
    PMID: 42403495 DOI: 10.3138/jammi-2025-0019
    BACKGROUND: Artificial Intelligence (AI) has emerged as a valuable tool in health care, supporting diagnostics and decision making. However, integration into clinical practice presents challenges, including data quality, accessibility, and regional guideline variations. This study evaluates four major AI platforms (ChatGPT, Meta AI, Copilot, and OpenEvidence) against CDC infection control guidelines for varicella and measles across Canada, Malaysia, and the United Kingdom.The objective was to assess the accuracy and consistency of AI responses on infection control measures compared with CDC guidelines and to evaluate how platforms handle complex scenarios.

    METHODS: A comparative analysis of the four AI platforms was conducted using structured questions and clinical case scenarios on varicella and measles. Responses were evaluated for alignment with CDC guidelines. Platform accessibility was tested from Canada, Malaysia, and the United Kingdom, and regional variations were analyzed.

    RESULTS: All platforms provided generally accurate information, but discrepancies were noted. ChatGPT and Meta AI mostly aligned with CDC guidelines, while OpenEvidence and Copilot omitted key epidemiological criteria. Meta AI lacked a full explanation of varicella laboratory criteria and was inaccessible in Malaysia. Regional differences in measles postexposure prophylaxis (PEP) were observed, particularly in Copilot and OpenEvidence. Response consistency varied between platforms.

    CONCLUSIONS: AI platforms show promise in supporting infection control but exhibit regional variability. Continued refinement of AI tools is essential to ensure their global applicability and accuracy. Consultation with an infection prevention and control (IPAC) physician remains vital for complex cases.

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