Advancing health care via artificial intelligence: From concept to clinic

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

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The potential for artificial intelligence in healthcare

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Applications of machine learning in drug discovery and development

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Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence

Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140 · see citing works

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works

Artificial intelligence in healthcare

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Artificial intelligence in medicine

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Artificial intelligence: A powerful paradigm for scientific research

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2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Artificial intelligence in drug discovery and development

Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works

Statistics versus machine learning

Bzdok, D., Altman, N., & Krzywinski, M. (2018) · Nature Methods · doi:10.1038/nmeth.4642 · Cited by 1,508 · see citing works

Artificial intelligence in drug development: Present status and future prospects

Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works

Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

Lee, D., & Yoon, S. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010271 · Cited by 1,017 · see citing works

Machine Learning in Drug Discovery: A Review

Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10058-4 · Cited by 843 · see citing works

Introduction to artificial intelligence in medicine

Mintz, Y., & Brodie, R. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1575882 · Cited by 818 · see citing works

A review on deep convolutional neural networks

Aloysius, N., & Geetha, M. K. (2017) · doi:10.1109/iccsp.2017.8286426 · Cited by 687 · see citing works

Artificial intelligence for clinical trial design

Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672 · see citing works

Advancing drug discovery via artificial intelligence

Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654 · see citing works

Artificial Intelligence in Healthcare: Review and Prediction Case Studies

Rong, G., Mendez, A., Assi, E. B., Zhao, B., & Sawan, M. (2020) · Engineering · doi:10.1016/j.eng.2019.08.015 · Cited by 573 · see citing works

Dropout vs. batch normalization: an empirical study of their impact to deep learning

Garbin, C., Xingquan Zhu, A., & Marques, O. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08453-9 · Cited by 573 · see citing works

REINVENT 2.0: An AI Tool for De Novo Drug Design

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Accelerating materials discovery using artificial intelligence, high performance computing and robotics

Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465 · see citing works

Machine Learning for Detection and Diagnosis of Disease

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A short guide for medical professionals in the era of artificial intelligence

Meskó, B., & Görög, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00333-z · Cited by 409 · see citing works

Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041676 · Cited by 367 · see citing works

A Review on Conventional Machine Learning vs Deep Learning

Chauhan, N. K., & Singh, K. P. (2018) · International Conference on Computing, Power and Communication Technologies (GUCON) · doi:10.1109/gucon.2018.8675097 · Cited by 354 · see citing works

Artificial Intelligence in Chemistry: Current Trends and Future Directions

Baum, Z. J., Yu, X., Ayala, P. Y., Zhao, Y., Watkins, S. P., & Zhou, Q. A. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00619 · Cited by 300 · see citing works

Artificial Intelligence in the 21st Century

Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., & Lee, I. (2018) · IEEE Access · doi:10.1109/access.2018.2819688 · Cited by 270 · see citing works

Open access in silico tools to predict the ADMET profiling of drug candidates

Kar, S., & Leszczyński, J. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1798926 · Cited by 248 · see citing works

Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here

Dankwa-Mullan, I., Rivo, M., Sepulveda, M., Park, Y., Snowdon, J., & Rhee, K. (2019) · Population Health Management · doi:10.1089/pop.2018.0129 · Cited by 217 · see citing works

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212 · see citing works

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

Allyn, J., Allou, N., Augustin, P., Philip, I., Martinet, O., Belghiti, M., Provenchère, S., Montravers, P., & Ferdynus, C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169772 · Cited by 188 · see citing works

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

Koromina, M., Pandi, M.-T., & Patrinos, G. P. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0151 · Cited by 140 · see citing works

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 138 · see citing works

Risk classification of cancer survival using ANN with gene expression data from multiple laboratories

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Improving User Experience of Virtual Health Assistants: Scoping Review

Curtis, R., Bartel, B., Ferguson, T., Blake, H. T., Northcott, C., Virgara, R., & Maher, C. A. (2021) · Journal of Medical Internet Research · doi:10.2196/31737 · Cited by 135 · see citing works

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

Mangold, C., Zoretic, S., Thallapureddy, K., Moreira, A., Chorath, K. T., & Moreira, A. G. (2021) · Neonatology · doi:10.1159/000516891 · Cited by 121 · see citing works

Opportunities and challenges using artificial intelligence in ADME/Tox

Bhhatarai, B., Walters, W. P., Hop, C. E. C. A., Lanza, G., & Ekins, S. (2019) · Nature Materials · doi:10.1038/s41563-019-0332-5 · Cited by 117 · see citing works

A Review of Wearable Internet-of-Things Device for Healthcare

Surantha, N., Atmaja, P., David, & Wicaksono, M. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.083 · Cited by 109 · see citing works

SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server

Olsen, L., Montefiori, M., Tran, K. P., & Jørgensen, F. S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz037 · Cited by 104 · see citing works

The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine?

Yang, Y., Zhang, J., Zang, G., & Pu, J. (2019) · PubMed Central · doi:10.11909/j.issn.1671-5411.2019.08.010 · Cited by 100 · see citing works

Artificial intelligence is improving the detection of lung cancer

Svoboda, E. (2020) · Nature · doi:10.1038/d41586-020-03157-9 · Cited by 91 · see citing works

Social Touch Gesture Recognition Using Convolutional Neural Network

Albawi, S., Bayat, O., Al-Azawi, S., & Uçan, O. N. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6973103 · Cited by 86 · see citing works

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug–drug interaction extraction and classification

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Artificial intelligence enabled applications in kidney disease

Chaudhuri, S., Long, A., Zhang, H., Monaghan, C. K., Larkin, J. W., Kotanko, P., Kalaskar, S., Kooman, J. P., SANDE, F. M. V. D., Maddux, F. W., et al. (2020) · Seminars in Dialysis · doi:10.1111/sdi.12915 · Cited by 55 · see citing works

Can Artificial Intelligence Improve the Management of Pneumonia

Chumbita, M., Cillóniz, C., Puerta‐Alcalde, P., Moreno-García, E., Sanjuan, G., García-Pouton, N., Soriano, À., Torres, A., & García‐Vidal, C. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9010248 · Cited by 54 · see citing works

Neural network adaptation process effectiveness dependent of constant training data availability

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Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation

Hall, A., Mitchell, A. R. J., Wood, L., & Holland, C. (2020) · Medicine · doi:10.1097/md.0000000000021388 · Cited by 47 · see citing works

Machine Learning and Deep Learning: A Comparative Review

Alaskar, H., & Saba, T. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-6307-6_15 · Cited by 46 · see citing works

Incorporating Artificial Intelligence Into Stroke Care and Research

Ding, L., Liu, C., Li, Z., & Wang, Y. (2020) · Stroke · doi:10.1161/strokeaha.120.031295 · Cited by 44 · see citing works

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Chauhan, K., Nadkarni, G. N., Fleming, F., McCullough, J. R., He, C., Quackenbush, J. F., Murphy, B. T., Donovan, M. J., Coca, S. G., & Bonventre, J. V. (2020) · Kidney360 · doi:10.34067/kid.0002252020 · Cited by 35 · see citing works

Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data

Rodríguez-Pérez, R., & Bajorath, J. (2018) · ACS Omega · doi:10.1021/acsomega.8b01682 · Cited by 30 · see citing works

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets

He, H., Liu, B., Luo, H., Zhang, T., & Jiang, J. (2020) · Stroke and Vascular Neurology · doi:10.1136/svn-2019-000323 · Cited by 22 · see citing works

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

Estépar, R. S. J. (2020) · Barcelona Respiratory Network · doi:10.23866/brnrev:2019-0014 · Cited by 14 · see citing works

The Ava bracelet for collection of fertility and pregnancy data in free-living conditions: An exploratory validity and acceptability study

Nulty, A. K., Chen, E. S., & Thompson, A. L. (2022) · Digital Health · doi:10.1177/20552076221084461 · Cited by 13 · see citing works

De Novo Design of Ligands Using Computational Methods

Suryanarayanan, V., Panwar, U., Chandra, I., & Singh, S. K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_5 · Cited by 12 · see citing works

Drugging the Undruggable: How Isoquinolines and PKA Initiated the Era of Designed Protein Kinase Inhibitor Therapeutics

Lorenz, R., Wu, J., Herberg, F. W., Taylor, S. S., & Engh, R. A. (2021) · Biochemistry · doi:10.1021/acs.biochem.1c00359 · Cited by 9 · see citing works

Artificial Intelligence in Biological Activity Prediction

Correia, J. V. M., Resende, T., Baptista, D., & Rocha, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-23873-5_20 · Cited by 8 · see citing works

Application of the HeartLander crawling robot for injection of a thermally sensitive anti-remodeling agent for myocardial infarction therapy

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The role of AI in drug discovery: challenges, opportunities, and strategies

Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works

Tribulations and future opportunities for artificial intelligence in precision medicine

Carini, C., & Seyhan, A. A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05067-0 · Cited by 175 · see citing works

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Singh, A. V., Bansod, G., Mahajan, M., Dietrich, P., Singh, S. P., Rav, K., Thißen, A., Bharde, A. M., Rothenstein, D., Kulkarni, S., et al. (2023) · ACS Omega · doi:10.1021/acsomega.3c00596 · Cited by 87 · see citing works

Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Alanazi, A. (2023) · Cureus · doi:10.7759/cureus.45255 · Cited by 87 · see citing works

Advancements and applications of Artificial Intelligence in cardiology: Current trends and future prospects

Olawade, D. B., Aderinto, N., Olatunji, G. D., Kokori, E., David-Olawade, A. C., & Hadi, M. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100109 · Cited by 79 · see citing works

Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care

Abualigah, L., Alomari, S. A., Almomani, M. H., Zitar, R. A., Saleem, K., Migdady, H., Snåšel, V., Smerat, A., & Ezugwu, A. E.-S. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06308-6 · Cited by 54 · see citing works

INTEGRATING ARTIFICIAL INTELLIGENCE AND BIG DATA IN MOBILE HEALTH: A SYSTEMATIC REVIEW OF INNOVATIONS AND CHALLENGES IN HEALTHCARE SYSTEMS

Mifa, A. A. &. A. F. (2024) · GLOBAL MAINSTREAM JOURNAL · doi:10.62304/jbedpm.v3i01.70 · Cited by 49 · see citing works

3D bioprinting and the revolution in experimental cancer model systems—A review of developing new models and experiences with in vitro 3D bioprinted breast cancer tissue-mimetic structures

Sztankovics, D., Moldvai, D., Petővári, G., Gelencsér, R., Krencz, I., Raffay, R. E., Dankó, T., & Sebestyén, A. R. (2023) · Pathology & Oncology Research · doi:10.3389/pore.2023.1610996 · Cited by 39 · see citing works

Exploring the Potential of ChatGPT-4 in Responding to Common Questions About Abdominoplasty: An AI-Based Case Study of a Plastic Surgery Consultation

Li, W., Chen, J., Chen, F., Liang, J., & Yu, H. (2023) · Aesthetic Plastic Surgery · doi:10.1007/s00266-023-03660-0 · Cited by 36 · see citing works

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members’ vision – part 1

Petrušić, I., Ha, W., Labastida‐Ramírez, A., Messina, R., Onan, D., Tana, C., & Wang, W. (2024) · The Journal of Headache and Pain · doi:10.1186/s10194-024-01847-7 · Cited by 36 · see citing works

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu, P. C., & Obulose, C. N. (2024) · Drug Development Research · doi:10.1002/ddr.22159 · Cited by 33 · see citing works

The role of artificial intelligence in preoperative planning for Total Hip Arthroplasty: a systematic review

Mozafari, J. K., Moshtaghioon, S. A., Mahdavi, S. M., Ghaznavi, A., Behjat, M., & Yeganeh, A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1417729 · Cited by 24 · see citing works

Continuous glucose monitoring data for artificial intelligence-based predictive glycemic event: A potential aspect for diabetic care

Ying, L. P., Yin, O. X., Quan, O. W., Jain, N., Mayuren, J., Pandey, M., Gorain, B., & Candasamy, M. (2024) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-024-01349-x · Cited by 19 · see citing works

Chatbots as Patient Education Resources for Aesthetic Facial Plastic Surgery: Evaluation of ChatGPT and Google Bard Responses

Garg, N., Campbell, D. J., Yang, A. A., McCann, A. C., Moroco, A. E., Estephan, L. E., Palmer, W. J., Krein, H. D., & Heffelfinger, R. N. (2024) · Facial Plastic Surgery & Aesthetic Medicine · doi:10.1089/fpsam.2023.0368 · Cited by 16 · see citing works

The AI-Powered Healthcare Ecosystem: Bridging the Chasm Between Technical Validation and Systemic Integration—A Systematic Review

Rahamtalla, B. M., Medani, I. E., Abdelhag, M. E., Eltigani, S. A., Rajan, S. K., Falgy, E., Hassan, N. M., Fadailu, M. E., Khudhayr, H. A., & Abdalla, A. (2025) · Future Internet · doi:10.3390/fi17120550 · Cited by 11 · see citing works

Artificial intelligence empowering rare diseases: a bibliometric perspective over the last two decades

Ou, P., Wen, R., Shi, L., Wang, J., & Liu, C. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03352-1 · Cited by 10 · see citing works

Human vs Machine: The Future of Decision-making in Plastic and Reconstructive Surgery

Duran, A., Demiröz, A., Çörtük, O., Ok, B., Özten, M., & Eroğlu, S. (2025) · Aesthetic Surgery Journal · doi:10.1093/asj/sjaf015 · Cited by 8 · see citing works

Artificial Intelligence and Machine Learning in Pharmaceutical Sciences: Unpacking Regulatory Guidance, Opportunities, and Challenges for Safe and Effective Drug DevelopmentPreprint

Uddin, M. R., Sovon, M. S. I., Mondal, S., Ahmed, S., Al-Mizan, M. A., Aktar, F., & Amran, M. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5505018 · Cited by 8 · see citing works

A review on the role of functional foods and derivatives for diabetes management

Nair, A., Anish, R. J., & Moorthy, S. N. (2025) · Journal of Food Science and Technology · doi:10.1007/s13197-025-06234-x · Cited by 7 · see citing works

Young Adult Perspectives on Artificial Intelligence–Based Medication Counseling in China: Discrete Choice Experiment

Zhang, J., Wang, J., Zhang, J. B., Xia, X., Zhou, Z.-Y., Zhou, X., & Wu, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/67744 · Cited by 7 · see citing works

Artificial intelligence in dentistry: insights and expectations from Swiss dental professionals

Donati, J., Beyer, M., Brokmeier, J., Neuhaus, K. W., Thieringer, F. M., & Berg, B.-­. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03066-9 · Cited by 7 · see citing works

Integrating natural non-pharmaceutical therapies into medical tourism: a dynamic health portrait-driven model for proactive older adult health and public health services

Liu, M., & Wang, B. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1580082 · Cited by 6 · see citing works

Evaluating a retrieval-augmented pregnancy chatbot: a comprehensibility–accuracy-readability study of the DIAN AI assistant

Valan, P., & Venugopal, P. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1640994 · Cited by 4 · see citing works

Artificial intelligence in natural products research

Yuan, X., Yang, X., Pan, Q., Luo, C. Y., Luan, X., & Zhang, H. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60902-2 · Cited by 3 · see citing works

Artificial intelligence-driven patient monitoring for adverse event detection in clinical trials

Vampana, S. B., Jayanthi, E. S. S., Mary, D. A., & Sriniketh, C. (2024) · International Journal of Basic & Clinical Pharmacology · doi:10.18203/2319-2003.ijbcp20241657 · Cited by 3 · see citing works

Future Trends and Challenges in Pharmaceutical Microbiology with the Integration of Artificial Intelligence

Vashishth, T. K., Sharma, V., Sharma, K. K., Vidyant, S., & Bhardwaj, A. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3212-2.ch012 · Cited by 2 · see citing works

Artificial Intelligence and the Use of Imaging Modalities in Biomedical Sciences

Ahanger, A. N., Nisa, B. U., Ahanger, A. B., Aalam, S. W., Bashir, ‏. S. M. O. A. B. ‏. S. M. O. A. , Macha, M. A., & Bhat, M. R. (2025) · Springer eBooks · doi:10.1007/978-981-96-8176-1_9 · Cited by 2 · see citing works

Fine-grained multimodal molecular pretraining via prompt learning

Li, Y., Wei, Z., Liu, C., & Wang, G. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114381 · Cited by 2 · see citing works

4A tetrahedron system: a synergistic framework for panvascular intervention empowered by flexible electronics

You, L., 瞿亦容, Chen, Y., Wang, Y., Shen, L., & Ge, J. (2026) · npj Flexible Electronics · doi:10.1038/s41528-026-00537-5 · Cited by 2 · see citing works

The use of artificial intelligence (AI) in pharmacology and the process of drug discovery

Pishva, S. P., & Mousavi, Z. (2024) · Qur̓ān/pizhūhī-i khāvar/shināsān · doi:10.61186/iau.34.2.105

Artificial Intelligence in Health Care from Oncology to Perioperative Care

Wani, S. Q., Khan, T., Wani, S. Y., & Farhat, D. (2024) · Journal of Radiation and Cancer Research · doi:10.4103/jrcr.jrcr_7_24

The impact of artificial intelligence on drug discovery for neuropsychiatric disorders

Agaram Sundaram, V., Saravanan, B., Bhavani Sowndharya, B., Muthu Chinnakannu Marimuthu, M., Munjal, K., & Chopra, H. (2025) · EXCLI journal · doi:10.17179/excli2025-8378

AI Data Applications in Drug Design, Development, and Diagnostics: A Comprehensive Approach to Modern Medicine

Singh, R., Pandey, P., Kumar, A., & Mishra, M. K. (2025) · Current Bioinformatics · doi:10.2174/0115748936370246250903052830

ARTIFICIAL INTELLIGENCE IN PRIMARY CARE - THREAT OR SUPPORT FOR THE GENERAL PRACTITIONER?

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