Medical specialists’ available time is usually limited and diseases might evolve and patient dynamics change over time making diagnostics a highly complex process. This not only applies to experienced professionals but also to actors with different or little expertise such as young assistant doctors. In addition, the clinical interpretation of medical information is a cognitively challenging task. This variety regularly leads to the diagnosis of disease becoming a side issue for healthcare experts. The healthcare system is a dynamic and changing environment and medical specialists continually face new challenges with changing responsibilities and frequent interruptions. The application of artificial intelligence (AI) provides advantages pertaining to the diagnosis of diseases.
However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it.
This paper extends academia by proposing a research agenda. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research.
The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients.
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