DeepDrug: Applying AI for the Advancement of Drug Discovery

Mukhopadhyay, Supratik; Bryliński, Michał; Bess, Adam; Berglind, Frej; Galliano, Chris; McGrew, Patrick F.

doi:10.1109/comsnets53615.2022.9668586

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Finally, if the drug candidate is approved by relevant regulatory bodies, it can be marketed and distributed. Throughout this process, AI is being ITM Web of Conferences 53, 01005 (2023) ICDSIA-2023 https://doi.org/10.1051/itmconf/20235301005 used to speed up the drug discovery and development process and automate routine tasks [13]. For example, AI can be used in molecular modelling software to predict how molecules interact with each other, or in virtual drug screening to rapidly assess the potential efficacy of large numbers of compounds.…”

Section: Drug Discovery and Developmentmentioning

confidence: 99%

The Potential Application of Artificial Intelligence in Healthcare and Hospitals

et al. 2023

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This study focuses on the potential application of Artificial Intelligence (AI) in healthcare and hospitals to improve the quality of services for patients. The research objectives include the investigation of existing AI use cases in healthcare, exploration of potential areas in which AI can best be applied, and identification of the challenges to successful AI application. This research utilizes both primary and secondary data sources to investigate the potential of AI in healthcare and hospitals. The primary data is collected through published research papers, technical reports, and industry news to gain an understanding of the current state of AI applications in healthcare. The secondary data is gathered from expert opinions with experienced healthcare professionals such as physicians, hospital administrators, and IT experts to gain insights into existing use cases and potential applications of AI in healthcare and hospitals. The results of the study demonstrate that AI has a significant potential to deliver enhanced outcomes in various aspects of healthcare and hospitals, including diagnosis, treatment, and management. However, the successful integration of AI requires overcoming numerous challenges such as regulatory standardization, privacy protection, and data availability. To foster a positive development of AI in healthcare, it is recommended that healthcare organizations enhance their digital capabilities, enable secure data sharing and collaboration, and use AI tools to deliver a more comprehensive and personalized patient care experience.

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Section: Drug Discovery and Developmentmentioning

confidence: 99%

The Potential Application of Artificial Intelligence in Healthcare and Hospitals

et al. 2023

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“…-Another significant responsibility in the medical field is assessing medication effects and forecasting how a drug will affect a human being. One essential method in this scenario is the combination of AI with computer-based medication creation and analysis [172,173].…”

Section: Ai-healthcare Integrationmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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DeepDrug: Applying AI for the Advancement of Drug Discovery

Cited by 2 publications

References 28 publications

The Potential Application of Artificial Intelligence in Healthcare and Hospitals

The Potential Application of Artificial Intelligence in Healthcare and Hospitals

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

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