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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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Omics Data and Data Representations for Deep Learning-Based Predictive Modeling. Int J Mol Sci 2022; 23:ijms232012272. [PMID: 36293133 PMCID: PMC9603455 DOI: 10.3390/ijms232012272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/03/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022] Open
Abstract
Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.
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Sun G, Dong D, Dong Z, Zhang Q, Fang H, Wang C, Zhang S, Wu S, Dong Y, Wan Y. Drug repositioning: A bibliometric analysis. Front Pharmacol 2022; 13:974849. [PMID: 36225586 PMCID: PMC9549161 DOI: 10.3389/fphar.2022.974849] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/12/2022] [Indexed: 11/14/2022] Open
Abstract
Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.
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Affiliation(s)
- Guojun Sun
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Dashun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Zuojun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Qian Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Hui Fang
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
| | - Chaojun Wang
- Hangzhou Aeronautical Sanatorium for Special Service of Chinese Air Force, Hangzhou, China
| | - Shaoya Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Shuaijun Wu
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Yichen Dong
- Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Yuehua Wan
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
- *Correspondence: Yuehua Wan,
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Artificial Intelligence in Biological Sciences. Life (Basel) 2022; 12:life12091430. [PMID: 36143468 PMCID: PMC9505413 DOI: 10.3390/life12091430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/25/2022] [Accepted: 09/10/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
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Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A, Patel K, Venkat V, Venkatesan C, Wable R, Dinatale M, Fu A, Iyer V, Kalove I, Kleyman M, Koutsoutis J, Menna D, Paliwal M, Patel N, Patel T, Rafique Z, Samadi R, Varadhan R, Bolla S, Vadapalli S, Ahmed Z. Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine. Front Genet 2022; 13:929736. [PMID: 35873469 PMCID: PMC9299079 DOI: 10.3389/fgene.2022.929736] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/25/2022] [Indexed: 12/13/2022] Open
Abstract
Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.
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Affiliation(s)
- Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Asude Berber
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mudassir Lodi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rihi Jain
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Achuth Nair
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Anirudh Pappu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Kush Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vignesh Venkat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Raghu Wable
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Matthew Dinatale
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Allyson Fu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vikram Iyer
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Ishan Kalove
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Marc Kleyman
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Joseph Koutsoutis
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - David Menna
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mayank Paliwal
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Nishi Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Thirth Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zara Rafique
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rothela Samadi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Roshan Varadhan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Shreyas Bolla
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States.,Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, United States
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Alrefaei AF, Hawsawi YM, Almaleki D, Alafif T, Alzahrani FA, Bakhrebah MA. Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross-sectional analysis of the Saudi human genome program. Sci Rep 2022; 12:1405. [PMID: 35082362 PMCID: PMC8791994 DOI: 10.1038/s41598-022-05296-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/07/2022] [Indexed: 12/21/2022] Open
Abstract
The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwide, is highly dependent on the Saudi population embracing the concept of participating in genetic testing. However, genetic data sharing and artificial intelligence (AI) in genomics are critical public issues in medical care and scientific research. The present study was aimed to examine the awareness, knowledge, and attitude of the Saudi society towards the SHGP, the sharing and privacy of genetic data resulting from the SHGP, and the role of AI in genetic data analysis and regulations. Results of a questionnaire survey with 804 respondents revealed moderate awareness and attitude towards the SHGP and minimal knowledge regarding its benefits and applications. Respondents demonstrated a low level of knowledge regarding the privacy of genetic data. A generally positive attitude was found towards the outcomes of the SHGP and genetic data sharing for medical and scientific research. The highest level of knowledge was detected regarding AI use in genetic data analysis and privacy regulation. We recommend that the SHGP’s regulators launch awareness campaigns and educational programs to increase and improve public awareness and knowledge regarding the SHGP’s benefits and applications. Furthermore, we propose a strategy for genetic data sharing which will facilitate genetic data sharing between institutions and advance Personalized Medicine in genetic diseases’ diagnosis and treatment.
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Affiliation(s)
- Abdulmajeed F Alrefaei
- Department of Biology, Genetic and Molecular Biology Central Lab, Jamoum University College, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
| | - Yousef M Hawsawi
- Research Centre, King Faisal Specialist Hospital and Research Centre, P.O. Box 40047, Jeddah, 21499, Saudi Arabia.,MBC: J04/ College of Medicine, Al-Faisal University, P.O. Box 50927, Riyadh, 11533, Kingdom of Saudi Arabia
| | - Deyab Almaleki
- Department of Evaluation, Measurement, and Research, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
| | - Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum, 25375, Saudi Arabia
| | - Faisal A Alzahrani
- Department of Biochemistry, Faculty of Science, Embryonic Stem Cells Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Muhammed A Bakhrebah
- King Abdulaziz City for Science and Technology (KACST), Life Science and Environment Research Institute, P.O. Box 6086, Riyadh, 11442, Saudi Arabia
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Menduti G, Rasà DM, Stanga S, Boido M. Drug Screening and Drug Repositioning as Promising Therapeutic Approaches for Spinal Muscular Atrophy Treatment. Front Pharmacol 2020; 11:592234. [PMID: 33281605 PMCID: PMC7689316 DOI: 10.3389/fphar.2020.592234] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Abstract
Spinal muscular atrophy (SMA) is the most common genetic disease affecting infants and young adults. Due to mutation/deletion of the survival motor neuron (SMN) gene, SMA is characterized by the SMN protein lack, resulting in motor neuron impairment, skeletal muscle atrophy and premature death. Even if the genetic causes of SMA are well known, many aspects of its pathogenesis remain unclear and only three drugs have been recently approved by the Food and Drug Administration (Nusinersen-Spinraza; Onasemnogene abeparvovec or AVXS-101-Zolgensma; Risdiplam-Evrysdi): although assuring remarkable results, the therapies show some important limits including high costs, still unknown long-term effects, side effects and disregarding of SMN-independent targets. Therefore, the research of new therapeutic strategies is still a hot topic in the SMA field and many efforts are spent in drug discovery. In this review, we describe two promising strategies to select effective molecules: drug screening (DS) and drug repositioning (DR). By using compounds libraries of chemical/natural compounds and/or Food and Drug Administration-approved substances, DS aims at identifying new potentially effective compounds, whereas DR at testing drugs originally designed for the treatment of other pathologies. The drastic reduction in risks, costs and time expenditure assured by these strategies make them particularly interesting, especially for those diseases for which the canonical drug discovery process would be long and expensive. Interestingly, among the identified molecules by DS/DR in the context of SMA, besides the modulators of SMN2 transcription, we highlighted a convergence of some targeted molecular cascades contributing to SMA pathology, including cell death related-pathways, mitochondria and cytoskeleton dynamics, neurotransmitter and hormone modulation.
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Affiliation(s)
| | | | | | - Marina Boido
- Department of Neuroscience Rita Levi Montalcini, Neuroscience Institute Cavalieri Ottolenghi, University of Turin, Turin, Italy
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Abstract
The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footing to identify the best drug combination to address the disease. In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described. The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health.
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Affiliation(s)
- Ambrish Saxena
- Indian Institute of Technology Tirupati, Tirupati, India
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