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The provided text describes the importance of Precision Medicine in cancer treatment and the role of Artificial Intelligence (AI) and Nanotechnology in achieving this goal. 

Here's a summary of the key points:
Introduction:
Precision medicine aims to tailor specific treatment regimes for each cancer patient by considering their unique molecular signatures, genetic and epigenetic characteristics. However, the high intra-tumor and inter-patient heterogeneities make it challenging to design diagnostic and therapeutic platforms effectively.

Nanotechnology's Contribution:
Nanomaterials have contributed significantly to the evolution of precision medicine. They enable fast and sensitive single-molecule detection, diagnostic assays based on nanosensors for biomarker detection, and nanomedicine-based cancer treatments. Advances in nanomedicine fabrication techniques and understanding cancer biology have led to the rational design of targeted therapies and theranostic nanomedicines that combine drugs and imaging agents for better treatment analysis.

AI in Medicine and Nanoinformatics:
AI, a branch of computer science, involves machines performing tasks requiring "human intelligence." Machine learning (ML), a subset of AI, utilizes algorithms trained on large datasets to find patterns and classify data. AI and ML have been used in various medical fields, including medical imaging and gene expression analysis. In nanoinformatics, AI and computational methods are applied for nanomaterial design and implementation.

AI Concepts and Algorithms:
The text briefly introduces some fundamental AI concepts and algorithms, such as supervised and unsupervised learning, artificial neural networks (ANN), support vector machine (SVM), decision tree learning, random forest classifier, principal component analysis (PCA), feature selection, Levenberg-Marquardt (LM) algorithm, Metropolis Monte Carlo Algorithm, and iterative stochastic elimination (ISE).

Overall, the combination of nanotechnology and AI shows promise in improving diagnostic accuracy and therapeutic outcomes in precision cancer medicine by overcoming the challenges posed by intra-tumor and inter-patient 

heterogeneities. The integration of AI in nanomedicine design helps optimize material properties and predict interactions with target drugs, biological fluids, immune systems, vasculature, and cell membranes, all of which impact therapeutic efficacy.

Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine is reviewed.
Keywords: nanotechnology, artificial intelligence, big-data, cancer, precision medicine
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1. Introduction
Every patient is unique. Alongside our apparent differences, such as age, gender, height, eye color and blood type, we also have unique molecular signatures. This leads to different phenotypic changes and varied drug-responses among patients.[1] Diversity among patients is especially apparent in different types of cancer, which are affected by accumulation of driver mutations leading to intra-tumor and inter-patient heterogeneities, complicating diagnosis and treatment.[2] Precision medicine aims to tailor a specific treatment regime to each patient by accounting for multiple genetic and epigenetic characteristics.[3]
Nanomaterials have contributed to the evolution of precision medicine, throughout all of the medical stages. New omics collection technologies, such as single-molecule nanopore sequencing, enable fast and sensitive single-molecule detection along with longer sequence read length, thus maintaining genetic context.[4] Diagnostic assays based on nanosensors allow biomarker detection in femtomolar concentrations as well as scanning for multiple disease biomarkers simultaneously in liquid biopsies (blood, urine, saliva) and in cell cultures.[5] Nanomedicine-based cancer treatments have been evolving over the past decades, from a population wide treatment approach, aimed primarily at improving efficacy and reducing side effects, to targeted systems that report about drug activity inside the patient's body.

Advances in nanomedicine fabrication techniques coupled with increased understanding of cancer biology, promoted the rational design of targeted therapy approaches utilizing endogenous and external stimuli for improved drug delivery. These advancements also supported the development of theranostic nanomedicines that combine a drug and an imaging agent to further analyze the treatment efficacy inside the patient's body.[6] Nevertheless, current nanosensors and targeted nanomedicine have had limited success in clinical translation in the field of cancer.[7] Artificial intelligence (AI, see Box 1) is a branch of computer science that relates to machines that perform tasks that require "human intelligence". Machine learning (ML, see Box 1), an area in AI, is an approach that trains an algorithm using large datasets of previous examples. It is applied in order to, inter alia, find patterns and classify data or find an optimal solution to a presented problem. Machine learning and AI in general have been used in different fields of medicine including medical imaging and analysis of gene expression patterns.[8] In nanoinformatics, AI and other computational methods are applied for nanomaterial design and implementation.[9]
Box 1
Basic Terms in Artificial Intelligence, Machine learning and Computational Models
Artificial intelligence (AI) –
The ability of machines to execute tasks that require "human intelligence", such as problem solving and learning. For example, an algorithm that learns to distinguish between healthy and diseased individuals.
Machine learning (ML) –
An area in AI based on construction of algorithms supplied with large datasets that are used as an input for training and improving the algorithm's output results. ML is used for numerous applications including decision making, classification and pattern recognition problems.
Supervised and Unsupervised Learning –
Supervised learning is a machine learning task in which the training data is already labeled with the required output and the algorithm modifies its variables in order to optimize the obtained results from the data as requested by the user. In unsupervised learning, the data is classified without prior labeling and categorization according to patterns discovered by the algorithm.
Artificial Neural Networks (ANN) –
An ANN is a framework of connected layers of nodes that can be used for implementing machine learning algorithms. It is composed of an input layer, an output layer, and usually also contains hidden layers. A node can receive input from multiple connections from the preceding layer, each assigned with a specific weight that is considered when calculating the node's output. The network is trained to optimize the weights of each node-to-node connection for achieving increased accuracy of the output.
Support Vector Machine (SVM) –
SVM is a machine learning algorithm that is trained to classify data by constructing an n-dimensional space according to the input features and optimizing the separation of different groups across this space.
Decision Tree Learning –
A decision tree is a method for data classification and regression that is based on constructing a tree-like structure that performs sequential tests on selected features.
Random Forest Classifier –
A Random Forest Classifier is built from a combination of decision trees. The randomness of this approach is due to a selection of the feature for testing from a random subset of the total features during the tree construction process, which leads to variant decision trees that comprise the forest.
Principal Component Analysis (PCA) –
PCA is a method that receives a dataset of examples with multiple features and uses linear combinations in order to generate a smaller number of new features called principal components (PCs). These PCs are arranged from top to bottom according to their variance, and therefore by using the top PCs the data can be classified and presented in lower dimensionality.
Feature Selection –
Feature selection is used in order to reduce the complexity of the problem by detecting the important features that contribute most to the results.
Levenberg–Marquardt (LM) Algorithm –
The LM algorithm is a fitting algorithm that is used for non-linear problems in an iterative process in order to minimize errors. It can be used in the training process of machine learning models.
Metropolis Monte Carlo Algorithm –
The Metropolis Monte Carlo Algorithm is used for randomly generating a set of configurations for an investigated system and calculate their probability distribution.
Iterative Stochastic Elimination (ISE) –
ISE is an algorithm for complex problem solving that uses stepwise variable scoring and rejection of the variables that lead to the worst results after each step, thus simplifying the problem.

Artificial Intelligence and Nanotechnology are two fields that have been instrumental in realizing the goal of Precision Medicine – tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nano-materials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intra-tumor and inter-patient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature and cell membranes, all affecting therapeutic efficacy.