Applications of artificial intelligence in stem cell therapy

Table 1 Key differences in artificial intelligence, machine learning and deep learning

Feature	AI	ML	DL	Ref.
Definition	It involves incorporating human intelligence into machines using algorithms and a set of rules	Enables computer systems to learn automatically from past events and improve accordingly without explicit programming	Use neural networks to learn from data	[9,13-15]
Subset relationship	A broader field encompassing ML and DL	A subset of AI	A subset of M	[9,13-15]
Functionality	Employs decision-making to exhibit intelligence	Uses algorithms to evaluate data, detect patterns, and make predictions. The system learns from data and improves over time	Analyzes data using multi-layered neural networks, producing output based on deep pattern recognition	[9,13-15]
Learning approach	Can be data-driven, knowledge-based, or rule-based	Relies on data-driven learning. Includes supervised, unsupervised, and reinforcement learning	Employs neural networks with hierarchical layers. Transforms simple features into abstract representations for better feature extraction	[9,13-15]
Human intervention	Requires human-defined rules and logic	Some human intervention is needed for data labeling and training	Minimal human intervention. Relies on self-managed learning processes	[9,13-15]
Data dependency	Can work with smaller datasets and predefined rules	Needs a moderate amount of structured data to learn effectively	Requires enormous volumes of labeled data for training	[9,13-15]
Processing power	Involves complicated arithmetic, search trees, and reasoning techniques	Involves complex algorithms and mathematical models	Requires high computational power due to deep neural networks	[9,13-15]
Efficiency	Efficiency is determined by the effectiveness of ML and DL components	More efficient than AI alone but less efficient than DL in handling large datasets	Highly efficient for processing large datasets due to automated feature extraction	[9,13-15]
Applications	Encompasses diverse subfields including natural language processing, computer vision, and robotics	Used in applications such as recommendation systems, and predictive analysis	Best suited for tasks like image recognition, speech recognition, and autonomous driving	[9,13-15]

AI: Artificial intelligence; ML: Machine learning; DL: Deep learning.

Table 2 Artificial intelligence tools and methods in stem cell therapy

Application in stem cell therapy	AI tools or methods
Behavior and characterization of stem cells	Deep learning, natural language processing and machine learning
Stem cell culture and differentiation	Convolutional neural networks and random forests
Prediction of mortality risk	Artificial neural networks, k-nearest neighbors, logistic regression, and decision trees
Stem cells imaging based classification	Deep learning and convolutional neural networks
Stem cell modeling	Machine learning algorithms
Drug discovery	DeltaVina, neural graph fingerprint, AtomNet, and DeepTox have been used in drug discovery
Optimization of the delivery method	Convolutional neural network

AI: Artificial intelligence.