Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

By Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan, Xinqi Dong. Database J. Biol. Databases Curation, 2020

753 citations recorded by the source corpus.

Abstract Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/653a7425e2b5e5a6c2777409b9e6788682a0b68c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michele Nappi
2. Abid Ishaq
3. Yang Xiang
4. Muhammad Umer
5. Seyedali Mirjalili
6. Ziqian Xie
7. Saima Sadiq
8. Cui Tao

Citations in the corpus

• Artificial Intelligence in Health Care: Bibliometric Analysis (2020, background): “These challenges include ethical issues brought up by AI application [45] and the quality and quantity of medical data and cases [46], which will decide the diagnosis and treatment ability of AI.”
• Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, background): “application while dealing with sensitive medical data [27].”
• Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design (2023, background): “As a result, there is a need for an improved interpretability and explainability of AI models, to ensure that their predictions can be understood and used effectively [221,222].”
• Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction (2020, background): “At present, it can improve the precision of diagnosis, enable disease prevention by early warning, streamline clinical decision making, and reduce healthcare costs [4-7].”
• Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification (2025, unknown): “Recent innovations in artificial intelligence (AI) and deep learning (DL) have emerged as transformative tools for analyzing medical data, enabling more accurate and timely diagnoses [6–8].”