Artificial intelligence-enabled healthcare delivery

Reddy, Sandeep; Fox, John; Purohit, Maulik P.

doi:10.1177/0141076818815510

Cited by 280 publications

(202 citation statements)

References 33 publications

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“…Clinical decision support is defined as computer programs that use clinical data and knowledge to support decisions made by healthcare professionals (Reddy, 2019). Clinical decision support systems may help to reduce medical errors and increase healthcare consistency and efficiency and efforts to get clinical decision support systems into routine practice are increasing.…”

Section: Clinical Decision Supportmentioning

confidence: 99%

Implications of Artificial Intelligence in Healthcare Delivery in the Hospital Settings: A Literature Reveiw

2019

IJPHCS

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Background: Artificial intelligence use in healthcare is increasing over the years. The aim of this review is to determine the positive and negative implications of artificial intelligence in healthcare delivery in hospital settings. Methods: Literature search was done using keywords "artificial intelligence", "hospital" and "implications" from databases of Scopus, Science Direct, Google scholar and PubMed. The literature search was limited to articles published in English, available in open access and articles that are able to be retrieved in full text format. Result: Artificial intelligence has positive influence on health care delivery especially in hospitals in terms of quality of care, efficiency and accuracy. On the other hand, artificial intelligence can also have negative implications in terms of ethical, privacy and dehumanization of care. Conclusion: In conclusion, the positive implication of artificial intelligence and the wide range opportunities for technological development exceed the negative aspect, especially when the negative aspects are addressed appropriately or avoided.

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Section: Clinical Decision Supportmentioning

confidence: 99%

Implications of Artificial Intelligence in Healthcare Delivery in the Hospital Settings: A Literature Reveiw

2019

IJPHCS

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“…With the availability of large datasets, such as whole genome sequencing, transcript profiling or HTS, artificial intelligence is expected to have major impacts on various aspects of biomedical research (Jiang et al, 2017;Wainberg et al, 2018;Reddy et al, 2019;Zhavoronkov et al, 2019). Application of AI to various areas of drug discovery would include ligand-based virtual screening (VS) (Mayr et al, 2016;Chen et al, 2018), target prediction (Mayr et al, 2018), structure-based virtual screening (Wallach et al, 2015), de novo molecular design (Kadurin, 2016;Aspuru-Guzik, 2018), or metabolomics approaches (Pirhaji et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

et al. 2020

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Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deeplearning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligandbinding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds' mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.

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“…As governments scramble to address these problems, technology-empowered solutions can help deal with the worldwide health crisis. Applications of innovative technologies such as Blockchain and Artificial Intelligence (AI) could have the answers in response to coronavirus crisis [4], [5], [6]. While blockchain can combat pandemics by enabling early detection of outbreaks, fast-tracking drug delivery, and protecting user privacy during the treatment, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

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Artificial intelligence-enabled healthcare delivery

Cited by 280 publications

References 33 publications

Implications of Artificial Intelligence in Healthcare Delivery in the Hospital Settings: A Literature Reveiw

Implications of Artificial Intelligence in Healthcare Delivery in the Hospital Settings: A Literature Reveiw

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

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