Bio-sampling alternative diagnostic imaging for telemedicine: a feasibility study of an AI-based throat camera for influenza

Introduction Telemedicine has progressed rapidly alongside advances in information and diagnostic technologies, providing benefits in accessibility, convenience, cost efficiency, and infection control. However, reliance on biological sample–based diagnostics, such as blood and urine tests, has limited the scope of fully remote diagnosis, confining many services to hybrid or in-clinic settings. Although home-based biological testing has partially addressed these constraints, fundamental barriers to complete remote diagnostic workflows remain. Methods This study evaluates the feasibility of bio-sampling alternative diagnostic imaging (BADI), an artificial intelligence–based diagnostic approach recently introduced in influenza testing. We developed a conceptual framework to compare conventional in-clinic testing, home-based biological testing, and BADI-based diagnostics across key dimensions relevant to remote medical implementation. Results The analysis identified four essential conditions for the transition of BADI-type diagnostics to remote healthcare delivery: (1) sufficient availability and accessibility for patients, (2) assurance of diagnostic accuracy and clinical reliability, (3) high usability and interpretive clarity for both patients and healthcare providers, and (4) seamless integration into medical systems with immediate linkage to appropriate treatment. Conclusion BADI demonstrates significant potential to overcome the limitations of biological sample–dependent diagnostics in telemedicine. Addressing the identified conditions is critical for enabling safe, scalable, and effective remote diagnosis and for advancing fully remote healthcare models.