IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research

Can the pharmaceutical industry reduce attrition rates?

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How many drug targets are there?

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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047

Big data analytics in healthcare: promise and potential

Raghupathi, W., & Raghupathi, V. (2014) · Health Information Science and Systems · doi:10.1186/2047-2501-2-3 · Cited by 3,034

Clinical development success rates for investigational drugs

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Edward Jenner and the History of Smallpox and Vaccination

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Cognitive computing

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The $2.6 Billion Pill — Methodologic and Policy Considerations

Avorn, J. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500848 · Cited by 322

The role of serendipity in drug discovery

Ban, T. A. (2006) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2006.8.3/tban · Cited by 294

National Health Expenditure Projections, 2014–24: Spending Growth Faster Than Recent Trends

Keehan, S. P., Cuckler, G. A., Sisko, A. M., Madison, A. J., Smith, S. D., Stone, D. A., Poisal, J. A., Wolfe, C. J., & Lizonitz, J. M. (2015) · Health Affairs · doi:10.1377/hlthaff.2015.0600 · Cited by 229

Routinedaten – das ungenutzte Potenzial in der Versorgungsforschung

Djalali, S., Markun, S., & Rosemann, T. J. (2017) · Praxis · doi:10.1024/1661-8157/a002630 · Cited by 10

Communicating About Precision Oncology

McFarland, D. C., Blackler, E., Banerjee, S., & Holland, J. (2017) · JCO Precision Oncology · doi:10.1200/po.17.00066 · Cited by 10

PAGE Study: Summary of a Study Protocol to Estimate the Prevalence of Severe Asthma in Spain Using Big Data Methods

Almonacid, C., Moreno, C. M., Gancedo, S. Q., Sánchez-Herrero, M. G., Gutiérrez, F. J. Á., Conejero, D. B., Cardona, V., & Soriano, J. B. (2020) · Journal of Investigational Allergology and Clinical Immunology · doi:10.18176/jiaci.0483 · Cited by 10

Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies

Silva, K. D., Enticott, J., Barton, C., Forbes, A. B., Saha, S. K., & Nikam, R. (2021) · Digital Health · doi:10.1177/20552076211047390 · Cited by 9

The cognitive function and the framework of the functional hierarchy

Gain, U. (2018) · Applied Computing and Informatics · doi:10.1016/j.aci.2018.03.003 · Cited by 11

Surgical Data Science: A Consensus PerspectivePreprint

Maier‐Hein, L., Eisenmann, M., Feldmann, C., Feußner, H., Forestier, G., Giannarou, S., Bernard Gibaud, A., Hager, G. D., Hashizume, M., Katić, D., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03184 · Cited by 22

Digitalization and Its Impact on Business

Matoušková, D. (2022) · Theory Methodology Practice · doi:10.18096/tmp.2022.02.03 · Cited by 21

Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance

Shahsavari, Y., Baseri, Y., Hafid, A. S., Dambri, O. A., & Makrakis, D. (2026) · Journal of Medical Internet Research · doi:10.2196/80178 · Cited by 24

Emotionally Based Strategic Communications in the Process of Societal Deradicalization

Ćosić, K., Srbljinović, A., & Popović, S. (2018) · International Journal of Strategic Communication · doi:10.1080/1553118x.2018.1426587 · Cited by 11

Greg, ML – Machine Learning for Healthcare at a Scale

Lapadula, P., Mecca, G., Santoro, D., Solimando, L., & Veltri, E. (2020) · Health and Technology · doi:10.1007/s12553-020-00468-9 · Cited by 9

IBM Watson AI-enhanced search tool identifies novel candidate genes and provides insight into potential pathomechanisms of traumatic heterotopic ossification

Foster, N., Wood, F. M., Fear, M. W., Pavlos, N. J., Raby, E., & Edgar, D. W. (2023) · Burns Open · doi:10.1016/j.burnso.2023.07.001 · Cited by 9

Sentiment Analysis and Mood Detection on an Android Platform Using Machine Learning Integrated with Internet of Things

Kushawaha, D., De, D., Mohindru, V., & Gupta, A. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-29407-6_18 · Cited by 9

Machine learning and data analytics

Pezoulas, V. C., Exarchos, T. P., & Fotiadis, D. I. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816507-2.00007-4 · Cited by 9

Drug Repurposing in Neurological Diseases: Opportunities and Challenges

Mao, X. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.93093 · Cited by 11

Extracting knowledge networks from plant scientific literature: potato tuber flesh color as an exemplary trait

Singh, G., Papoutsoglou, E., Keijts-Lalleman, F., Vencheva, B., Rice, M., Visser, R. G. F., Bachem, C., & Finkers, R. (2021) · BMC Plant Biology · doi:10.1186/s12870-021-02943-5 · Cited by 10

A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer

Krentel, F., Singer, F., Rosano-González, M. L., Gibb, E. A., Liu, Y., Davicioni, E., Keller, N., Stekhoven, D. J., Julio, M. K., & Seiler, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-85151-3 · Cited by 9

IBM Watson Industry Cognitive Education Methods

Rajeshwari, M., & Krishna Prasad, K. (2020) · International Journal of Case Studies in Business IT and Education · doi:10.47992/ijcsbe.2581.6942.0066 · Cited by 9

Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms

Rai, D., & Thakkar, H. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85117-6.00003-0 · Cited by 9

Data-Based Value Co-Creation in Smart Service Systems: A Reinterpretation of Customer Journey

Grimaldi, M., Ciasullo, M. V., Troisi, O., & Castellani, P. (2020) · International Journal of Business and Management · doi:10.5539/ijbm.v15n6p212 · Cited by 9

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements

Gupta, D., Wal, P., Wal, A., R, S., Kumar, M., Panda, K. C., & Sharma, M. C. (2024) · Current Drug Discovery Technologies · doi:10.2174/0115701638314252241016165345 · Cited by 8

The evolution of personalized medicine: publications review

Rykov, M. Y. (2022) · Problems of Social Hygiene Public Health and History of Medicine · doi:10.32687/0869-866x-2022-30-6-1211-1219 · Cited by 11

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique

Tyagin, I., & Safro, I. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05812-8 · Cited by 8

Las principales tecnologías de la era de la industria 5.0

Pérez-Domínguez, L. A. (2024) · Revista Ingenio · doi:10.22463/2011642x.4352 · Cited by 17

A Hybrid Approach to Identifying Key Factors in Environmental Health Studies

Dong, S., Feric, Z., Li, X., Rahman, S. M., Li, G., Wu, C., Gu, A. Z., Dy, J., Kaeli, D. R., Meeker, J. D., et al. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622049 · Cited by 8

Inteligência artificial: avanços e tendências

Cozman, F. G., Plonski, G. A., & Neri, H. (2021) · Universidade de São Paulo. Instituto de Estudos Avançados eBooks · doi:10.11606/9786587773131 · Cited by 54

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Shankar, P. R. V., & Anderson, N. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1641215 · Cited by 8

Getting Started on Artificial Intelligence in Health Care and Clinical Research: Includes Rigor Checklist for Authors and Reviewers

Sen, C. K., & DeMazumder, D. (2025) · Advances in Wound Care · doi:10.1177/21621918251380217 · Cited by 9

Innovative artificial intelligence tools: exploring the future of healthcare through IBM Watson's potential applications

Norouzi, K., Ghodsi, A., Argani, P., Andi, P. A., & Hassani, H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36370-2.00028-1 · Cited by 8

Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI)

Carreras, J., Ikoma, H., Kikuti, Y. Y., Nagase, S., Ito, A., Orita, M., Tomita, S., Tanigaki, Y., Nakamura, N., & Masugi, Y. (2025) · Cancers · doi:10.3390/cancers17152428 · Cited by 9

Artificial Intelligence in Cardiovascular Diseases

Bashir, S., Ahanger, A. N., Assad, A., Bhat, M. R., & Macha, M. A. (2025) · Springer eBooks · doi:10.1007/978-981-96-8176-1_10 · Cited by 8

Awareness about the future of artificial intelligence in healthcare among medical students in Kancheepuram District, Tamil Nadu

Parvathavarthine, C. R., Krishna, M. K. P., Janaki, C. S., Sophia, M., & Chandar, S. N. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns8.13571 · Cited by 7

Introduction to artificial intelligence for cardiovascular clinicians

Chang, A. C., & Limon, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00010-x · Cited by 12

Enhancing the Willingness of Adopting AI in Education Using Back-propagation Neural Networks

Wu, C., Hung, C.-H., Shen, C., & Yu, J. (2024) · Sensors and Materials · doi:10.18494/sam4485 · Cited by 7

Use and perception of generative artificial intelligence in traditional Korean medicine education: A cross-sectional survey of undergraduate students in Korea

Shin, S., Sang, J., & Yoo, J. K. (2025) · Integrative Medicine Research · doi:10.1016/j.imr.2025.101216 · Cited by 7

Advancing Cognitive Cities with the Web of Things

D’Onofrio, S., Franzelli, S., & Portmann, E. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-58190-3_5 · Cited by 7

The Future Medical Science and Colorectal Surgeons

Kim, Y. J. (2017) · Annals of Coloproctology · doi:10.3393/ac.2017.33.6.207 · Cited by 7

Patent portfolio management: literature review and a proposed model

de Jesus, C. K. C., & Salerno, M. S. (2018) · Expert Opinion on Therapeutic Patents · doi:10.1080/13543776.2018.1472238 · Cited by 7

A New Perspective on Education in the Digital Age

Tække, J., & Paulsen, M. (2021) · Bloomsbury Publishing Plc eBooks · doi:10.5040/9781350175426 · Cited by 23

A Systematic Approach to Configuring MetaMap for Optimal Performance

Jing, X., Indani, A., Hubig, N., Min, H., Gong, Y., Cimino, J. J., Sittig, D. F., Rennert, L., Robinson, D., Biondich, P., et al. (2022) · Methods of Information in Medicine · doi:10.1055/a-1862-0421 · Cited by 7

Cognitive Robotics on 5G Networks

Lv, Z., Qiao, L., & Wang, Q. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3414842 · Cited by 7

An overview of Big Data in Healthcare: multiple angle analyses

Gou, X., & Xu, Z. (2022) · Journal of Smart Environments and Green Computing · doi:10.20517/jsegc.2021.07 · Cited by 7

Intelligent Fusion

Jadwal, P. K., & Saini, H. K. (2025) · Wiley eBooks · doi:10.1002/9781394271382.ch3 · Cited by 7

From Anatomy to Algorithm: Scope of AI-Assisted Diagnostic Competencies in Health Sciences Education

Galdames, I. S. (2024) · International Journal of Medical and Surgical Sciences · doi:10.32457/ijmss.v11i3.2818 · Cited by 7

Personalized medicine: current trends and prospects

Dolgopolov, I. S., & Rykov, M. Y. (2022) · Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics) · doi:10.21508/1027-4065-2022-67-4-14-21 · Cited by 6

An Efficient Ophthalmic Disease QA System Integrated with Knowledge Graphs and Digital Humans

Guo, S., Guo, J., Wang, H., Wang, H., Huang, X., & Zhang, L. (2024) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp62589.2024.10809040 · Cited by 6

Helping organizations manage the innovation process to join the Cognitive era

Olmos-Sánchez, K. M., & Rodas-Osollo, J. (2020) · International Conference in Software Engineering Research and Innovation (CONISOFT) · doi:10.1109/conisoft50191.2020.00012 · Cited by 6

Cognitive User Interface for Portfolio Optimization

He, Y., Romanko, O., Sienkiewicz, A., Seidman, R., & Kwon, R. H. (2021) · Journal of risk and financial management · doi:10.3390/jrfm14040180 · Cited by 8

Big Data Analytics and Cognitive Computing: A Review Study

Matoori, S. S., & Nourafza, N. (2021) · Cited by 6

Artificial intelligence–enabled clinical trials might be a faster way to perform rapid clinical trials and counter future pandemics: lessons learned from the COVID-19 period

Chakraborty, C., Bhattacharya, M., Dhama, K., & Agoramoorthy, G. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000088 · Cited by 6

Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context

Noll‐Hussong, M. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.7054 · Cited by 6

A practical use of expert system "AI-Q" focused on creating training data

Noguchi, T., Hashizume, Y., Moriyama, H., Gauthier, L., Ishikawa, Y., Matsuno, T., & Suganuma, A. (2018) · International Conference on Business and Industrial Research (ICBIR) · doi:10.1109/icbir.2018.8391169 · Cited by 6

The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective

Karapiperis, C., Chasapi, A., Angelis, L., Scouras, Z. G., Mastroberardino, P. G., Tapio, S., Atkinson, M. J., & Ouzounis, C. (2020) · Big Data · doi:10.1089/big.2019.0144 · Cited by 6

A decision support engine for infill drilling attractiveness evaluation using rule-based cognitive computing under expert uncertainties

Mao, Q., Ma, X., & Wang, Y. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109671 · Cited by 6

The evolution of knowledge on genes associated with human diseases

Dias, T. L., Dalmolin, R. J. S., Amaral, P., Alves, T. L., Schuch, V., Franco, G. R., & Nakaya, H. I. (2021) · iScience · doi:10.1016/j.isci.2021.103610 · Cited by 5

Drug Delivery in Biotechnology: Present and Future

Yadav, S., Palei, N. N., Dinda, S. C., & Dhar, A. K. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_7 · Cited by 9

AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation ApproachPreprint

Sybrandt, J., Tyagin, I., Shtutman, M. S., & Safro, I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05635 · Cited by 6

The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competenciesPreprint

Saheb, T., & Amini, B. (2021) · Research Square · doi:10.21203/rs.3.rs-714137/v1 · Cited by 6

Everything is ObviousPreprint

Abbott, R. B. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3056915 · Cited by 6

Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning

Davis, D. S., Ritchie, M., Hammill, D., Garrett, J., Slater, R. O., Otoo, N., Orlov, A., Gosling, K. M., Price, J., Yip, D., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1211064 · Cited by 5

The practical use of artificial intelligence in Transfusion Medicine and Apheresis

Anstey, C., Ullman, D., Su, L., Su, C., Siniard, R. C., Simmons, S. C., Edberg, J., & Williams, L. A. (2024) · Transfusion and Apheresis Science · doi:10.1016/j.transci.2024.104001 · Cited by 5

Data platforms for open life sciences–A systematic analysis of management instruments

Laufs, D., Peters, M., & Schultz, C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276204 · Cited by 6

The application of cognitive neuroscience to judicial models: recent progress and trends

Zhang, N., & Zhang, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1257004 · Cited by 5

Digital technology administrative penalties and green technology innovation: Evidence from China

Li, H., & Chen, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41159 · Cited by 5

Impact de l’intelligence artificielle dans la recherche clinique et la collecte de données en vie réelle

Chaix, B. (2018) · Actualités Pharmaceutiques · doi:10.1016/j.actpha.2018.05.012 · Cited by 5

Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology

Kiyosawa, N., & Manabe, S. (2016) · The Journal of Toxicological Sciences · doi:10.2131/jts.41.sp15 · Cited by 5

Towards an Affective Computational Model for Machine Consciousness

Chandra, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70139-4_91 · Cited by 5

Smart Government: Die Verwaltung und den Staat der Zukunft denken

Demaj, L. (2018) · Informatik-Spektrum · doi:10.1007/s00287-018-1098-x · Cited by 5

Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma

Agoston, D. V. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809556-0.00004-6 · Cited by 7

Towards Digital Cancer Genetic Counseling

Abujamous, L., Tbakhi, A., Odeh, M., Alsmadi, O. A., Kharbat, F. F., & Abdel‐Razeq, H. (2018) · International Conference on Cancer Care Informatics (CCI) · doi:10.1109/cancercare.2018.8618229 · Cited by 5

Artificial intelligence designed drug synthesis: One-pot preparation of trans β-lactams and application to cholesterol absorption inhibitor SCH 47949 synthesis

Takabatake, T., Yoneda, T., Otsuka, J., Kagawa, N., & Toyota, M. (2019) · Tetrahedron Letters · doi:10.1016/j.tetlet.2019.07.033 · Cited by 5

A Personal Medical Digital Assistant Agent for Supporting Human Operators in Emergency Scenarios

Croatti, A., Montagna, S., & Ricci, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71679-4_15 · Cited by 5

Neural Mechanisms of High‐Level Vision

Hegdé, J. (2018) · Comprehensive physiology · doi:10.1002/cphy.c160035 · Cited by 6

Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review

Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4

Cognitive Internet of Things: A Review of Theory, Applications, and Recent Advances

Giuliano, A., McCafferty-Leroux, A., Yawney, J., & Gadsden, S. A. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3615461 · Cited by 9

Artificial Intelligence for Bone: Theory, Methods, and Applications

Yuan, D., Zhang, H., Tong, L., & Chen, D. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500070 · Cited by 8

Harnessing Artificial Intelligence (AI) for a greener future: a review of AI advancements in green chemistry, chemical processes and sustainable materials

Elkhatat, A. M., & Al‐Muhtaseb, S. A. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07326-7 · Cited by 5

The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B

Deng, N., & Zhang, Q. (2021) · Symmetry · doi:10.3390/sym13071185 · Cited by 4

Tourism Recommender System Based on Natural Language Classifier

Tenemaza, M., Limaico, J., & Luján‐Mora, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80624-8_29 · Cited by 4

Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins

Coskuner‐Weber, O., Gentili, P. L., & Uversky, V. N. (2025) · Biophysical Reviews · doi:10.1007/s12551-025-01286-x · Cited by 4

Video Intelligence as a Component of a Global Security System

Verdejo, D., & Mercier‐Laurent, E. (2019) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-29904-0_10 · Cited by 4

An Efficient Approach towards Enhancing the Performance of m-Health Using Sensor Networks and Cloud Technologies

Mishra, K. N. (2020) · Springer eBooks · doi:10.1007/978-3-030-37468-6_26 · Cited by 4

Artificial Intelligence–Enhanced Drug Discovery and the Achievement of Next-Generation Human-Centered Health System

Mbatha, S. Z., Mulaudzi, T. V., Mbita, Z., Adeola, H. A., Batra, J., Blenman, K., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-36461-7_7 · Cited by 5

Cognitive Services for Collaborative mHealth: The OnParkinson Case Study

Macedo, P., Madeira, R. N., & Camarinha-Matos, L. M. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-99127-6_38 · Cited by 4

Data openness and democratization in healthcare

Lewis, K., Pham, C., & Batarseh, F. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818366-3.00006-x · Cited by 4

Acting with and against Big Data in School and Society: The Big Democratic Questions of Big Data

Paulsen, M., & Tække, J. (2020) · The Journal of Communication and Media Studies · doi:10.18848/2470-9247/cgp/v05i03/15-31 · Cited by 4

Visual Analytic Decision-Making Environments for Large-Scale Time-Evolving Graphs

Venna, S. R., Gottumukkala, R. N., & Raghavan, V. V. (2016) · Handbook of statistics · doi:10.1016/bs.host.2016.07.002 · Cited by 4

Healthcare in the 21st century

Krittanawong, C. (2016) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2016.11.002 · Cited by 4

Genomics data integration for knowledge discovery using genome annotations from molecular databases and scientific literature

Singh, G. (2019) · doi:10.18174/505685 · Cited by 4

Cognitive Computing

Pradhan, N., Singh, A. S., & Singh, A. (2020) · doi:10.1002/9781119640554.ch2 · Cited by 4

Computational thinking through the lens of biological evolution learning: enhancing understanding through the levels of biological organization and computational complexity

Christensen, D., & Lombardi, D. (2024) · Evolution Education and Outreach · doi:10.1186/s12052-024-00202-3 · Cited by 4

Assisting, Replicating, or Autonomously Acting? An Ethical Framework for Integrating AI Tools and Technologies in Healthcare

Padela, A. I., Hayek, R., Tabassum, A., Jotterand, F., & Qadir, J. (2025) · Bioethics · doi:10.1111/bioe.70019 · Cited by 3

Desenvolvimento de protótipo de chatbot para avaliação da maturação da fístula arteriovenosa

Benevides, A. B., Correia, B. R., Zaccaro, K. R. L., Campos, J. F., Souto, J. D. S. S., Adba, Y. B., & Brandão, M. A. G. (2023) · Acta Paulista de Enfermagem · doi:10.37689/acta-ape/2023ao012322 · Cited by 4

Digital decision support for structural improvement of melanoma tumor boards: using standard cases to optimize workflow

Hoier, D., Groß-Ophoff-Müller, C., Franklin, C., Hallek, M. J., von Stebut, E., Elter, T., Mauch, C., Kreuzberg, N., & Koll, P. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-024-05627-3 · Cited by 3

Artificial Intelligence and Machine Learning in Drug Discovery

Shah, P., Thakkar, D. V., Panchal, N., & Jha, R. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2897-2.ch003 · Cited by 3

Drug repurposing for regenerative medicine and cosmetics: Scientific, technological and economic issues

Vu, T.-D., Luong, D. T., Ho, T.-T., Thi, T. M. N., Singh, V., & Chu, D. (2024) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2024.02.005 · Cited by 3

AI-assisted models to predict chemotherapy drugs modified with C 60 fullerene derivatives

Robles-Hernández, J.-S.-L., Medina, D. I., Aguirre-Hurtado, K., Bosquez, M., Salcedo, R., & Miralrio, A. (2024) · Beilstein Journal of Nanotechnology · doi:10.3762/bjnano.15.95 · Cited by 4

A semiotic-inspired machine for personalized multi-criteria intelligent decision support

Neto, F. B. D. L., Martins, D. M. L., & Vossen, G. (2018) · Data & Knowledge Engineering · doi:10.1016/j.datak.2018.07.012 · Cited by 3

Deep Neural Network for Whole Slide Vein Segmentation

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