Havana Syndrome refers to a constellation of neuropsychiatric signs and symptoms that have been classified as anomalous health incidents (AHIs). First reported by personnel working at the US Embassy in Havana, Cuba, in 2016, presentation includes sudden-onset vertigo, feeling of pressure in the head, cognitive dysfunction, tinnitus, autonomic disturbances, and postural instability. It is now widely accepted that directed energy exposure is the most probable cause. The diagnostic process for Havana Syndrome AHIs remains complex and multifactorial, and herein we propose that the use of modular artificial intelligence approaches, particularly those leveraging machine learning and predictive analytics, can integrate multidimensional data from a standardized evaluative protocol employing neuroimaging, cognitive testing, auditory/vestibular assessments, and biomarker analyses to more accurately, effectively and efficiently identify patterns indicative of AHI. 

We opine that such use of AI with standardized diagnostic protocols would mitigate variability across cases and institutions, and would ensure ethical integrity in patient care. However, we note that the integration of AI necessitates stringent biocybersecurity measures to protect sensitive patient data from potential breaches, and propose methods toward sustaining safety and integrity when employing these methods.

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