Review
Biochemistry & Molecular Biology
Elettra Barberis, Shahzaib Khoso, Antonio Sica, Marco Falasca, Alessandra Gennari, Francesco Dondero, Antreas Afantitis, Marcello Manfredi
Summary: This review discusses the application of recent technological innovations in mass spectrometry to metabolomics analysis, with a focus on the use of artificial intelligence (AI) strategies. The article also explores the challenges and limitations of implementing metabolomics-AI systems, as well as recent tools and studies in disease classification and biomarker identification.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Review
Biotechnology & Applied Microbiology
Yihao Liu, Minghua Wu
Summary: Deep learning has been successfully applied to various tasks in different fields, including disease diagnosis in medicine. By extracting multilevel features from medical data, deep learning helps doctors automatically assess diseases and monitor patients' physical health.
BIOENGINEERING & TRANSLATIONAL MEDICINE
(2023)
Review
Health Care Sciences & Services
Farida Mohsen, Balqees Al-Saadi, Nima Abdi, Sulaiman Khan, Zubair Shah
Summary: Precision medicine has the potential to revolutionize cardiovascular diseases by tailoring treatment strategies to individual characteristics. Artificial intelligence (AI) is increasingly being applied in various areas of cardiovascular medicine, including diagnosis, prognosis, risk prediction, and treatment planning.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Review
Biology
Dominic D. Martinelli
Summary: Recent research suggests that machine learning algorithms can generate novel drug-like molecules, revolutionizing drug discovery. This systematic literature review examines machine learning models, challenges, and encoding methods in molecular design. The review identified six prominent algorithms and eight central challenges. Statistical analysis and visualization show the evolution of machine learning approaches in drug design over the past five years. Future opportunities and reservations are also discussed.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Oncology
Dennis Jun Jie Poon, Li Min Tay, Dean Ho, Melvin Lee Kiang Chua, Edward Kai-Hua Chow, Eugenia Li Ling Yeo
Summary: Tumor radioresistance poses a major challenge in cancer treatment, with strategies involving understanding molecular mechanisms and utilizing combinatorial treatment approaches. Conventional drug-screening methods have proven inefficient for discovering new drug combinations.
Review
Health Care Sciences & Services
Francesco Bonomi, Silvia Peretti, Gemma Lepri, Vincenzo Venerito, Edda Russo, Cosimo Bruni, Florenzo Iannone, Sabina Tangaro, Amedeo Amedei, Serena Guiducci, Marco Matucci Cerinic, Silvia Bellando Randone
Summary: Machine learning shows promising applications in systemic sclerosis, including early diagnosis, classification, and treatment prediction, offering new possibilities for precision medicine.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Review
Pharmacology & Pharmacy
Fabio Boniolo, Emilio Dorigatti, Alexander J. Ohnmacht, Dieter Saur, Benjamin Schubert, Michael P. Menden
Summary: Precision medicine aims to treat diseases based on environmental factors, lifestyles, and molecular profiles of patients. Currently, it only uses a few molecular biomarkers for decision-making, but future applications are expected to leverage artificial intelligence to capture the full molecular landscape of patients.
EXPERT OPINION ON DRUG DISCOVERY
(2021)
Review
Biology
Ramkumar Thirunavukarasu, C. George Priya Doss, R. Gnanasambandan, Mohanraj Gopikrishnan, Venketesh Palanisamy
Summary: Precision Medicine utilizes patients' genomic profiles and healthcare data to provide personalized medical outcomes. Deep learning models significantly influence precision medicine research due to their ability to handle large volumes of data and identify inherent features. This review emphasizes the importance of deep learning-based analytical models in handling big data in precision medicine research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Neurosciences
Antonio Del Casale, Giuseppe Sarli, Paride Bargagna, Lorenzo Polidori, Alessandro Alcibiade, Teodolinda Zoppi, Marina Borro, Giovanna Gentile, Clarissa Zocchi, Stefano Ferracuti, Robert Preissner, Maurizio Simmaco, Maurizio Pompili
Summary: This systematic review summarizes the latest advancements of machine learning (ML) applied to pharmacogenomics in psychiatry. The results show that ML techniques have great potential in personalized therapy and drug response prediction, especially when genetic and biodemographic information are integrated with clinical profiles.
CURRENT NEUROPHARMACOLOGY
(2023)
Review
Pharmacology & Pharmacy
Anuraj Nayarisseri, Ravina Khandelwal, Poonam Tanwar, Maddala Madhavi, Diksha Sharma, Garima Thakur, Alejandro Speck-Planche, Sanjeev Kumar Singh
Summary: Artificial Intelligence has revolutionized the drug development process by quickly identifying potential biologically active compounds. Machine Learning tools and algorithms, such as SVM and RF, are being used at various stages of drug designing and development to improve efficiency and accuracy. Successful cases have demonstrated the effectiveness of these models in identifying novel compounds and predicting activities for disease treatments.
CURRENT DRUG TARGETS
(2021)
Review
Oncology
Sameer Quazi
Summary: The advancement of precision medicine improves healthcare by enhancing diagnostics, customizing treatments, and predicting early disease risks. Scrutinizing overall patient data and broad factors helps differentiate between ill and relatively healthy individuals, leading to the most appropriate path for precision medicine.
Review
Oncology
Lin Shui, Haoyu Ren, Xi Yang, Jian Li, Ziwei Chen, Cheng Yi, Hong Zhu, Pixian Shui
Summary: Radiogenomics utilizes medical imaging data and individual genetic information to construct prediction models for guiding treatment strategies and evaluating clinical outcomes. Despite some issues that need to be addressed, radiogenomics represents a repeatable and cost-effective approach for detection, offering a promising alternative for invasive interventions.
FRONTIERS IN ONCOLOGY
(2021)
Review
Medicine, General & Internal
Charlotte J. J. Haug, Jeffrey M. M. Drazen
Summary: This article introduces the history of artificial intelligence in medicine, its applications in image analysis, disease outbreak identification, and diagnosis, as well as the use of chatbots.
NEW ENGLAND JOURNAL OF MEDICINE
(2023)
Review
Cardiac & Cardiovascular Systems
Evangelos K. Oikonomou, Rohan Khera
Summary: Artificial intelligence and machine learning have the potential to revolutionize healthcare, particularly in the management of diabetes and its cardiovascular complications. This review provides an overview of the various data-driven methods and their application in personalized care for diabetes patients at increased cardiovascular risk. The article discusses the role of artificial intelligence in diagnosis, prognostication, phenotyping, and treatment, as well as the challenges and ethical considerations that arise. It also emphasizes the need for regulatory standards to ensure the effectiveness and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
CARDIOVASCULAR DIABETOLOGY
(2023)
Review
Pharmacology & Pharmacy
R. S. K. Vijayan, Jan Kihlberg, Jason B. Cross, Vasanthanathan Poongavanam
Summary: Artificial intelligence is playing a crucial role in drug discovery, from target identification to preclinical development. This review provides an overview of current AI technologies and presents real impact examples, while discussing the opportunities and challenges of adopting AI in drug discovery.
DRUG DISCOVERY TODAY
(2022)