AIMC Topic: Artificial Intelligence

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A performance evaluation of drug response prediction models for individual drugs.

Scientific reports
Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC]) of an individual drug by inputting pharmaco...

A nystagmus extraction system using artificial intelligence for video-nystagmography.

Scientific reports
Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography ha...

Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles.

Fertility and sterility
OBJECTIVE: To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed intercourse (TI) in natural cycles.

Deploying Artificial Intelligence for Thoracic Imaging Around the World.

Journal of the American College of Radiology : JACR
PURPOSE: Artificial intelligence (AI) thoracic imaging applications are increasingly being deployed in low- and middle-income countries (LMICs). Radiologists have a critical gatekeeping role to ensure the effective and ethical implementation of AI so...

Artificial intelligence based multimodal language decoding from brain activity: A review.

Brain research bulletin
Decoding brain activity is conducive to the breakthrough of brain-computer interface (BCI) technology. The development of artificial intelligence (AI) continually promotes the progress of brain language decoding technology. Existent research has main...

Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review.

Archives of gerontology and geriatrics
BACKGROUND: Older adults have been disproportionately affected by the COVID-19 pandemic. This scoping review aimed to summarize the current evidence of artificial intelligence (AI) use in the screening/monitoring, diagnosis, and/or treatment of COVID...

Artificial intelligence & clinical nutrition: What the future might have in store.

Clinical nutrition ESPEN
Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interve...

Molecular bases of comorbidities: present and future perspectives.

Trends in genetics : TIG
Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under...

PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction.

Journal of medicinal chemistry
The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be full...