AIMC Topic: Precision Medicine

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Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.

Molecular diversity
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing...

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.

The AAPS journal
The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artifi...

Deep learning in histopathology: the path to the clinic.

Nature medicine
Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been de...

Integrating Social Determinants of Health to Precision Medicine through Digital Transformation: An Exploratory Roadmap.

International journal of environmental research and public health
The COVID-19 pandemic has altered healthcare delivery platforms from traditional face-to-face formats to online care through digital tools. The healthcare industry saw a rapid adoption of digital collaborative tools to provide care to patients, regar...

Artificial intelligence in oncology: Path to implementation.

Cancer medicine
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are de...

Digital Twins for Multiple Sclerosis.

Frontiers in immunology
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adj...

Artificial intelligence for clinical oncology.

Cancer cell
Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of m...

Opportunities and challenges for artificial intelligence in clinical cardiovascular genetics.

Trends in genetics : TIG
A combination of emerging genomic and artificial intelligence (AI) techniques may ultimately unlock a deeper understanding of heterogeneity and biological complexities in cardiovascular diseases (CVDs), leading to advances in prognostic guidance and ...