How to Collect and Interpret Medical Pictures Captured in Highly
Challenging Environments that Range from Nanoscale to Hyperspectral
Imaging



Digital well-being records are multimodal and high-dimensional (HD). Better theradiagnostics stem from new computationally thorough and edgy technologies, i.e., hyperspectral (HSI) imaging,
super-resolution, and nanoimaging, but advance mess data portrayal and retrieval. A patient's state
involves multiple signals, medical imaging (MI) modalities, clinical variables, dialogs between clinicians and patients, metadata, genome sequencing, and signals from wearables. Patients' high volume,
personalized data amassed over time have advanced artificial intelligence (AI) models for higherprecision inferences, prognosis, and tracking. AI promises are undeniable, but with slow spreading and
adoption, given partly unstable AI model performance after real-world use. The HD data is a ratelimiting factor for AI algorithms generalizing real-world scenarios. This paper studies many health
data challenges to robust AI models' growth, aka the dimensionality curse (DC). This paper overviews
DC in the MIs' context, tackles the negative out-of-sample influence and stresses important worries for
algorithm designers. It is tricky to choose an AI platform and analyze hardships. Automating complex
tasks requires more examination. Not all MI problems need automation via DL. AI developers spend
most time refining algorithms, and quality data are crucial. Noisy and incomplete data limits AI, requiring time to handle control, integration, and analyses. AI demands data mixing skills absent in regular systems, requiring hardware/software speed and flexible storage. A partner or service can fulfill
anomaly detection, predictive analysis, and ensemble modeling.
