Modifica di Logic of medical language (sezione)

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{{ArtBy|
| autore = Gianni Frisardi
| autore2 = Riccardo Azzali
| autore3 = Flavio Frisardi
}}
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'''Abstract''': Medical language plays a crucial role in clinical diagnosis but often leads to ambiguity and diagnostic challenges due to its limited semantic scope. Terms like "orofacial pain" can vary widely in meaning depending on the specialist interpreting them. For example, a neurologist might interpret it as neuropathic pain, while a dentist might focus on temporomandibular disorders (TMD). This ambiguity stems from the hybrid nature of medical language, which incorporates technical terms from both formal logic (e.g., mathematics, electrophysiology) and natural language, leading to inconsistencies in understanding.

This chapter explores the complexities of medical language by examining the clinical case of Mary Poppins, a patient with long-term orofacial pain. Her symptoms were diagnosed differently by various specialists, demonstrating how ambiguity in terms like "TMD" and "neuropathic pain" can lead to conflicting diagnoses. We address the need for a more precise and standardized approach to medical terminology, particularly in cases where multiple systems (e.g., masticatory and nervous systems) interact.

Furthermore, the concept of "encrypted machine language" is introduced as a metaphor for how the human body communicates complex information through symptoms and test results. This information, often conveyed through non-verbal signals such as electrophysiological tests, must be decrypted by clinicians to provide an accurate diagnosis. The chapter also highlights the importance of interdisciplinary approaches, combining inputs from different fields to reduce diagnostic errors and enhance patient care.

By addressing the limitations of medical language and emphasizing the integration of both verbal and machine-derived data, this chapter paves the way for a more nuanced understanding of clinical diagnostics. It calls for further exploration of how medical language can be refined to improve diagnostic precision, ultimately leading to better patient outcomes.