Loading

International Journal of Clinical Anesthesiology

Artificial Intelligence in Anesthesia: What Might the Future Hold?

Mini Review | Open Access | Volume 12 | Issue 1

  • 1. Assistant Professor, Anesthesiology and Peri-operative medicine. Hershey Medical Centre, Hershey, PA, USA
+ Show More - Show Less
Corresponding Authors
Shubha Srinivasareddy, Assistant Professor, Anesthesiology and Peri-operative medicine. Hershey Medical Centre, Hershey, PA, USA
ABSTRACT

Integrating Artificial Intelligence (AI) into anesthesia has transformed perioperative care offering enhanced precision, real-time decision support, and effective predictive analytics beyond human capabilities. AI is now being frequently used in preoperative assessments, intraoperative monitoring, postoperative management, and has improved patient safety and outcomes. AI implementation is not without challenges, and certain issues persist, such as data quality, algorithmic transparency, potential biases, and ethical concerns that are related to patient privacy and patient autonomy, which pose significant hurdles to clinicians. Beyond its limitations, AI’s potential has also revolutionized anesthesia immensely. AI has promised for bright future where anesthetic care will be more adapted and effective.

KEYWORDS
  • Artificial intelligence
  • Anesthesia
  • Machine learning
  • Patient safety
  • Perioperative care
  • Predictive analytics
  • Healthcare technology
CITATION

Srinivasa reddy S (2024) Artificial Intelligence in Anesthesia: What Might the Future Hold. Int J Clin Anesthesiol 12(1): 1131.

INTRODUCTION

Artificial intelligence AI integration in anesthesia is an evolution of medical technology and is promising to redefine anesthesia care [1]. Transformative potentials of AI inspire hope for a future where anesthesia is safe, efficient and personalized [2]. In conventional times, anesthesia has relied heavily on the expertise and anesthesiologists’ judgments to ensure patient safety, manage perioperative processes as well as optimize postoperative outcomes [3]. AI has introduced new possibilities, rapidly extending beyond human competencies and offering enhanced precision, predictive analytics, and real-time decision support to anesthesiologists [4]. Along with leveraging Machine Learning (ML) algorithms, neural networks and other AI- based algorithms, the potential for individualized, data-driven anesthesia care is rapidly becoming increasingly tangible [5].

OBJECTIVE

This paper aims to critically examine AI’s role, current applications, efficiency, and what the future holds for us in the field of anesthesia. We aim to examine the current state of AI in anesthesia, explore future innovations, and discuss ethical and regulatory challenges, needed for navigation for successful AI integration in anesthesiology.

AI IN ANAESTHESIA

AI’s role in anesthesia is multifaceted and encompass preoperative evaluation, intraoperative monitoring, and postoperative management [4]. Predictive models analyses patient-specific data—such as age, comorbidities, and surgical requirements—to tailor anesthetic plans and minimize adverse events risks [6]. AI systems are adept at real-time monitoring and can continuously assess vital signs and other physiological parameters while enabling early detection of potential complications and facilitating timely interventions [7].

Using AI in anesthesia has not been without challenges. Data quality, the explanation of algorithms, potential for bias are all complicated challenges. Such biases may stem from the data that AI systems and such biased treatment allocation will be deemed unfair to the concerned patient groups. Additionally, questions regarding the patient’s privacy and the degree of anesthesiologists’ decision-making independence become crucial while using AI applications in practice [8].

Preoperative assessment is the critical aspect in anesthesiology when clinicians aim to identify and mitigate perioperative risks through a thorough evaluation of the medical history of surgical patients, physical examination, and relevant laboratory tests [5]. Like in the past when complex tasks with risk stratification were based on factors such as chronic lung disease, severe asthma, and smoking status and were relied only on clinical expertise of anesthesiologists are now being handled by AI. AI and Machine Learning (ML) into this process are now revolutionizing how preoperative assessments are conducted and are offering unprecedented levels of precision and efficiency.

Some research in preoperative assessment include, the ASA- PS classification for preoperative risk assessment has been used for most surgical patients. However, the limitations of the ASA- PS classification include the subjective nature of the clinicians’ evaluations and high inter-rater variability [9]. Accurate perioperative risk stratification is important for facilitating shared decision-making and the allocation of medical resources. Several preoperative risk scores have been developed and used in clinical practice, including the American Society of Anesthesiologists Physical Status (ASA-PS) classification [10], American College of Surgeons National Surgical Quality Improvement Program (ACS- NSQIP) surgical risk calculator [11], surgical Apgar score [12], and Risk Stratification Index [13].

AI-enhanced preoperative assessment systems use vast datasets from previous surgeries for example patient’s specific variables, surgical details, and post-operative outcomes of previous surgeries [10]. With ML algorithms, Solanki SL, et al.

[14] suggest AI analyzes this data and generates data-driven recommendations for anesthetic management, which adhere strictly to each patient’s unique risk profile. For instance, AI can predict likeliness of postoperative complications also such as respiratory failure or acute kidney injury and provide information suggesting adjustments to the anesthetic plan for risk mitigation. This invaluable predictive capability of AI resolves complex cases where traditional methods overlook subtle risk factors [15].

AI’s abilities in processing and learning from large datasets allow it to refine its predictive models and improve correctness over time endlessly. Studies demonstrated the efficacy of neural network classifiers of ML such as Multilayer Perceptrons, have achieved risk classification accuracy rates as high as 97.79% [16]. This level of precision exceeds what is typically achievable through manual assessments alone, positioning AI as a critical tool in enhancing patient safety and surgical procedures outcomes [17]. Challenges include the need for demanding validation of AI-generated recommendations against the clinical judgment of experienced anesthesiologists [18]. Ethical considerations, such as data privacy and algorithmic transparency, also require careful attention [19].

Recent advances in Artificial Intelligence (AI) have led to significant progress in predicting preoperative complications in anesthesia. Hill et al. [19] created a random forest model using data from 53,097 patients, achieving an AUROC of 0.93, outperforming traditional scores like the ASA-PS and POSPOM. Bertsimas D, et al. [20] introduced a surgical risk calculator that uses optical classification trees to predict outcomes in 382,960 patients, with an AUROC of 0.92. Lee CK, et al. [21] developed a deep learning model with 45 intraoperative features reaching an AUROC of 0.91. Fritz BA, et al. [22] built a convolutional neural network that analyzed time-series data from 95,907 patients using 54 preoperative parameters and achieved influential results. Traditional Revised Cardiac Risk Index (RCRI) remains popular but shows moderate accuracy with an AUROC of 0.75, while in contrast, Bihorac A, et al.’s [23] My SurgeryRisk model (2019) based on generalized additive models predicted cardiovascular complications with an AUROC of 0.85.

Mathis et al. used gradient boosting machines that are predicting heart failure post-surgery with an AUROC of 0.87 based on 499 preoperative and 263 intraoperative data points. For predicting acute kidney injury (AKI), Rank’s recurrent neural network model outperforms experienced clinicians in post- cardiothoracic surgery predictions. Another gradient boosting model by Lee A, et al. [3]. has also proved its high efficacy with an AUROC of 0.90. All these AI models show predictive tools potentials in anesthesia and now are being frequently used, offering more accurate risk assessments and personalized care [24]. AI has brought major shift in how anesthesia is monitored and managed during surgery and advanced systems make real- time data interpretation smoother, pulling together information from sources like ECGs, blood pressure monitors, and anesthesia machines [25]. For instance, AI-powered tool continuously scans ECG readings and is able to quickly spotting irregular heartbeats and in the same, it alert anesthesiologists before they become serious problems [26]. Deep learning models also help predict blood pressure drops or other instabilities by analyzing data from both invasive and non-invasive devices which is now enabling timely interventions. One exciting development is AI-guided anesthesia systems, which automatically adjust drug levels on their own in real-time, ensuring patients are getting the required dose while reducing side effects. Decision support tools, just like Hill et al. [19] created using machine learning to offer predictive guidance and suggest changes during surgery ultimately boosting patient safety and the effectiveness of anesthesia [24].

AI-driven enhancements in surgical logistics and training for anesthesia

AI is changing more than just patient care in anesthesia as now it’s reshaping how surgeries are organized and how anesthesiologists are trained. In the operating room, AI connects devices like anesthesia machines and patient monitors and it is allowing them to communicate smoothly, which streamlines entire workflow. By automating routine tasks AI helps reduce human errors and making procedures safer while also making better use of resources. This is especially important in complex surgeries where quick, accurate information sharing can make a big difference [17].

Beyond improving logistics, AI is transforming how anesthesiologists learn. Augmented Reality (AR) are the tools powered by AI, create realistic training environments where practitioners can practice techniques like nerve blocks in a safe and controlled setting. These tools provide instant feedback helping trainees perfect their skills. AI-driven simulators can replicate rare or difficult cases and giving anesthesiologists the confidence to handle unexpected situations. As AI continues to develop, its impact on both surgical coordination and the education of anesthesia professionals is quickly growing for its smarter solutions that enhance both surgical outcomes and the skills of those in the field [17].

Ethical and regulatory challenges of AI

AI integration in anesthesia brings about several challenges and ethical dilemmas that must be navigated with caution to ensure these technologies are used safely and fairly in clinical settings. Major concerns are biases present in AI models and these biases can creep in at different stages, from the initial data collection to the final deployment of the models and they may sometimes lead to unfair outcomes especially among groups that are underrepresented [12]. For example, AI systems that are trained mostly on data from white males may struggle to predict accurate outcomes for women or people of color which can lead to misdiagnoses and less effective care.

Another area of concern is privacy and security of patient data as AI models often rely on large datasets such as Electronic Health Records (EHRs) to generate predictions and recommendations and using such data raises questions about patient consent, data ownership, and the potential for data breaches. Patients must be fully informed about how their data will be used and stringent measures could be in place for their privacy protection and prevent unauthorized access [17]. The lack of transparency in AI decision-making processes problematic. Many AI systems operate as “black boxes,” making it difficult for clinicians to understand how decisions are made and this opacity can lead to over-reliance on AI systems undermine clinical judgment and reducing the role of human oversight in patient care, and when AI systems produce recommendations, clinicians who wish to challenge these recommendations often need to provide substantial evidence and this could be challenging gather than the data used by the AI system itself [17]. AI’s potential to disrupt traditional roles and responsibilities within the healthcare profession also raises some concerns about job displacement and professional autonomy. As AI systems become more integrated into clinical practice, so there is a risk of anesthesiologists being replaced by AI in certain tasks traditionally performed by anesthesiologists leading to skill degradation which may compromise quality-care. Balancing the benefits of AI with the need to maintain human expertise and oversight is crucial to ensuring that AI enhances rather than detracts from patient care.

Future prospects and innovations in anesthesia

Recent innovations in AI and anesthesia are transforming anesthetic practice and the most promising development is the integration of AI-driven algorithms for real-time monitoring and prediction of patient outcomes during surgery. These algorithms utilize vast datasets including patient vitals and their medical history along with intraoperative data to predict potential complications such as hemodynamic instability or adverse events like hypoxia. The ability to anticipate and preemptively emphasize that these issues enhance patient safety and reduce the burden on anesthesiologists [13].

Another development at the forefront of modern medicine is the use of AI in ultrasound guidance for regional anesthesia and vascular access. AI algorithms are now trained enough to automatically identify anatomical landmarks and structures that are complicated and cannot be recognized by humans easily such as nerves and blood vessels. In this way AI is improving the accuracy and speed of procedures like nerve blocks and central line placements reducing overall surgery time, Viderman D, et al. [9] suggested.

It is also used to control anesthetic deliveries since there are systems that use predictions of anesthetic needs to adjust drug dosages continuously. For instance, closed-loop integrated smart anesthesia delivery systems automatically modulate the anesthetic doses administered to patients using AI technology, considering the emerging physiological data of the client. This result in better control of the depth of anesthesia to avoid over or under-sedation [1]. AI’s capabilities are expected to grow further in the context of anesthesia as new and better models embracing genomic and proteomic data are worked out. Such models could potentially provide individualised anesthetic regimens according to the patient’s genetic profile so that the best drugs and dosing could be used improving chances of success and minimizing complications [27].

Authors thought on implementation of AI in anesthesia training and practice

Although the above-described AI-based predictive models showed high predictive performance in various perioperative settings, most of the results were obtained from single-centre retrospective studies. Before applying AI models in training and clinical practice, additional external and prospective validation and randomized clinical trials are required. Reinforcement learning models may suggest optimal strategies to overcome inter-individual variability; however, their clinical utility must be verified. If the high performance of AI algorithms is well maintained in future studies, they can be widely used in clinical practice as a powerful tool to help clinicians improve patient safety and outcomes.

CONCLUSION

AI’s integration into anesthesia holds transformative potential and now is offering enhanced patient safety, precision, and efficiency in surgical units. Notable challenges persists like algorithmic transparency, potential biases, and ethical concerns around privacy and clinical autonomy, which must be carefully managed. As AI is evolving continuedly, its role in anesthesia will likely expand in near future leading to more personalized and data-driven anesthetic care. To realize these benefits, the medical communities must try to resolve ethical and regulatory challenges while ensuring that AI complements rather than compromises clinical expertise and patient trust. AI future in anesthesia is promising but still, its success hinges on careful, ethical integration.

REFERENCES
  1. Singam A. Revolutionizing patient care: A comprehensive review of artificial intelligence applications in anesthesia. Cureus. 2023; 15: e49887.
  2. Hamilton A. The future of artificial intelligence in surgery. Cureus. 2024; 16: e63699.
  3. Lee A. An evidence-based strategy for the use of simulation to assess situation awareness in applicants to nurse anesthesia programs. 2024.
  4. Görmü? SK. Integrative artificial intelligence in regional anesthesia: Enhancing precision, efficiency, outcomes and limitations. Journal of Innovative Healthcare Practices. 2024; 5: 52-66.
  5. Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Curr Opin Anaesthesiol. 2024; 37: 413-420.
  6. Zeng S, Qing Q, Xu W, Yu S, Zheng M, Tan H, et al. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Front Med. 2024; 11: 1365524.
  7. Dabas M, Gefen A. Application of machine learning algorithms to diagnosis and prognosis of chronic wounds. Big Data Analysis and Artificial Intelligence for Medical Sciences. 2024: 43-57.
  8. Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi Journal of Anaesthesia. 2024; 18: 249-256.
  9. Viderman D, Dossov M, Seitenov S, Lee MH. Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review. Front Med. 2022; 9: 994805.
  10. Singhal M, Gupta L, Hirani K. A comprehensive analysis and review of artificial intelligence in anaesthesia. Cureus. 2023; 15: e45038.
  11. Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring artificial intelligence in anesthesia: A primer on ethics, and clinical applications. Surgeries. 2023 ; 4: 264-274.
  12. Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health. 2019; 9: 010318.
  13. Sarraf E, Ramaswamy P. Exploring technological innovation and its incorporation into clinical practice. Anesthesiology News. 2023.
  14. Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol. 2021; 27: 2758-2770.
  15. Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi J Anaesth. 2022; 16: 86-93.
  16. Karpagavalli S, Jamuna KS, Vijaya MS. Machine learning approach for preoperative anaesthetic risk prediction. International Journal of Recent Trends in Engineering, 2009; 1: 19-22.
  17. Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring artificial intelligence in anesthesia: A primer on ethics, and clinical applications. Surgeries. 2023; 4: 264-274.
  18. Kumar A, Seewal R, Jain D, Kaur R. Framework for personalized chronic pain management: Harnessing AI and personality insights for effective care. Journal of Artificial Intelligence and Technology. 2024; 4: 132-144.
  19. Hill. Automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. 2018.
  20. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical Risk is not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg. 2018; 268: 574-583.
  21. Lee CK, Hofer I, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality. Anesthesiology. 2018; 129: 649- 662.
  22. Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019; 123: 688-695.
  23. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: Development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019; 269: 652-662.
  24. Yoon HK, Yang HL, Jung CW, Lee HC. Artificial intelligence in perioperative medicine: A narrative review. Korean J Anesthesiol. 2022; 75: 202-215.
  25. Liu X, McGrath S, Flanagan C, Lei Y, Zeng L. Perioperative anesthesia data: Visualization, effects, analysis with artificial intelligence. In: 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB. IEEE. 2024: 746-751.
  26. Ardon A, Chadha R, George III J. Post-discharge care and monitoring: What’s new, what’s controversial. Current Anesthesiology Reports. 2024; 14: 299-305.
  27. Banerjee S, Abhishek HN, Gupta P, Patel AP, Kant K, Shetti AN. Artificial intelligence in anesthesia: Biotechnology applications for optimal patient outcomes. Journal of Cellular Biotechnology.2023; 9: 85-91.

Srinivasareddy S (2024) Artificial Intelligence in Anesthesia: What Might the Future Hold? Int J Clin Anesthesiol 12(1): 1131.

Received : 06 Sep 2024
Accepted : 05 Oct 2024
Published : 10 Oct 2024
Journals
Annals of Otolaryngology and Rhinology
ISSN : 2379-948X
Launched : 2014
JSM Schizophrenia
Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
Journal of Substance Abuse and Alcoholism
ISSN : 2373-9363
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Bioinformatics, Genomics and Proteomics
ISSN : 2576-1102
Launched : 2014
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
Journal of Sleep Medicine and Disorders
ISSN : 2379-0822
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
Journal of Cardiology and Clinical Research
ISSN : 2333-6676
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
Annals of Biometrics and Biostatistics
ISSN : 2374-0116
Launched : 2013
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
Author Information X