Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare
Abstract
:1. Introduction
2. Role of Artificial Intelligence in Healthcare
2.1. Disease Detection and Diagnosis and Medical Imaging
2.2. Treatment Planning and Personalized Medicine
2.3. Drug Discovery and Development
2.4. Predictive Analytics and Risk Assessment
3. Literature Review
3.1. Methodology
3.2. Results
3.2.1. AI in Cardiology
3.2.2. AI in Dermatology
3.2.3. AI in Gastroenterology
3.2.4. AI in Neurology and Neuroscience
3.2.5. AI in Ophthalmology
3.2.6. AI in Psychiatry
3.2.7. AI in Forensics and Toxicology
3.2.8. AI in Radiology
3.2.9. AI in Surgery
3.2.10. AI in Pathology
3.2.11. AI in Urology
3.2.12. AI in Obstetrics and Gynecology
AI in Obstetrics
AI in Gynecology
4. Discussion and Challenges
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Term | Definition |
---|---|
Artificial Intelligence (AI) | The first definition was been given in 1950 by Alan Turing, the founding father of AI, as the science and engineering of making intelligent machines, especially intelligent computer programs [3]. According to Salto-Tellez M. et al. [4], AI represents a range of advanced machine technologies that can derive meaning and understanding from extensive data inputs, in ways that mimic human capabilities. In the present context of medical practice, a specific definition may be a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [5]. |
Machine Learning (ML) | ML, a subset of artificial intelligence, exhibits the experiential “learning” associated with human intelligence, while also having the capacity to learn and improve its analyses through the use of computational algorithms [6,7]. Alpaydin E. [8] defined machine learning as the field of programming computers to optimize a performance criterion using example data or past experience. ML-based tools are used in the healthcare system to provide various treatment alternatives and individualized treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care [9]. |
Deep Learning (DL) | Deep Learning, a subset of Machine Learning, refers to a deep neural network, which is a specific configuration where neurons are organized in multiple successive layers that can independently learn representations of data and progressively extract complex features, performing tasks such as computer vision and natural language processing (NLP) [10]. In experimental settings across multiple medical specialties, DL performs equivalently to healthcare professionals for detecting diseases from medical imaging [11]. |
Natural Language Processing (NLP) | Natural Language Processing is a theoretically-motivated range of computational techniques for analyzing and representing naturally-occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications [12]. NLP techniques have been used to structure information in healthcare systems by extracting relevant information from narrative texts so as to provide data for decision-making [13]. |
Robotics | The robot has been defined as “a reprogrammable multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks” by the Robot Institute of America [14]. The term “robotics” refers to the study and use of robots. Robotic assistance has been shown to improve the safety and performance of intracorporeal suturing, which is heavily required in urological and gynecological procedures [15]. |
Artificial Neural Network (ANN) | An Artificial Neural Network, a subset of Machine Learning, is a computational model inspired by the biological neural networks in the human brain. These systems are mainly used for pattern identification and processing and are able to progressively improve their performance based on analytic results from previous tasks [16,17,18]. Many areas have been integrating the use of ANNs to facilitate the diagnosis, prognosis, and treatment of many diseases [19,20,21]. |
Convolutional Neural Network (CNN) | A Convolutional Neural Network is a Deep Learning algorithm specifically designed for image and video processing, primarily used in medical image analysis and diagnostics. CNNs have demonstrated superior performance as compared with classical machine learning algorithms and in some cases achieved comparable or better performance than clinical experts [22]. |
Medical Specialty | Year of Study | Author | Application |
---|---|---|---|
Cardiology | 2019 | Attia Z.I. [38] | Screening for cardiac contractile dysfunction |
2019 | Attia Z.I. [39] | Detection of left ventricular systolic dysfunction | |
2018 | Alsharqi M. [40] | Echocardiography analysis | |
2017 | Weng S.F. [41] | Cardiovascular risk prediction | |
Dermatology | 2020 | Young A.T. [42] | Diagnosis of skin lesions |
2019 | Dick V. [43] | Diagnosis of melanoma | |
2017 | Esteva A. [44] | Classification of skin cancer | |
Gastroenterology | 2021 | Kröner P.T. [45] | Detection of various lesions |
2020 | Martin D.R. [46] | Detecting current Helicobacter pylori infection | |
Neurology and Neuroscience | 2020 | Pedersen M. [47] | Diagnosis of neurological diseases |
2017 | Hazlett H.C. [48] | Diagnosis of autism | |
2020 | Ienca M. [49] | Diagnosis of Alzheimer’s disease | |
Ophthalmology | 2017 | Rathi S. [50] | Teleophthalmology for retinopathy and glaucoma |
2016 | Gulshan V. [51] | Detection of diabetic retinopathy | |
2017 | Long E. [52] | Diagnosis of congenital cataracts | |
Psychiatry | 2022 | Pham K.T. [53] | Classification of psychiatric disorders |
2017 | Vieira S. [54] | Classification of schizophrenia patients | |
2018 | Loh E. [1] | Prediction of suicide attempts | |
Forensics and Toxicology | 2022 | Wankhade T.D. [55] | Detection of various samples |
2021 | Thurzo A. [56] | Identification of a cadaver | |
2020 | Chary M.A. [57] | Identification of drug use patterns | |
Radiology | 2018 | Hosny A. [58] | Recognition of complex radiographic patterns |
2016 | Chen H. [59] | Detection in ultrasonography | |
2017 | Ghafoorian M. [60] | Segmentation in magnetic resonance imaging (MRI) | |
2017 | Wang H. [61] | Classification of mediastinal lymph node metastasis | |
Surgery | 2020 | Zhou X.Y. [62] | Advances in surgery |
2018 | Hu Y. [63] | Robotic sewing and knot tying | |
2019 | Hu Y. [64] | Suturing robot for transanal endoscopic microsurgery | |
2016 | Shademan A. [65] | Robotic soft tissue surgery | |
Pathology | 2021 | Cui M. [66] | Digitizing histopathology |
2019 | Niazi M.K.K. [67] | Whole-slide imaging | |
2017 | FDA [68] | IntelliSite Pathology Solution | |
2019 | FDA [69] | Summary Aperio AT2 DX system | |
2017 | Araújo T. [70] | Classification of breast cancer | |
2017 | Tumeh P.C. [71] | Identification of the immune cell populations | |
2019 | Bera K. [72] | Quantitative evaluation of histological and morphological patterns | |
2018 | Mezheyeuski A. [73] | Classification of lung cancer patients | |
2020 | Balázs A. [74] | Detection of metastasis and micrometastasis | |
2019 | Shaban M. [75] | Prediction of disease-free survival in oral squamous cell carcinoma | |
2019 | Hekler A. [76] | Classification of histopathological melanoma images | |
2014 | Dong F. [77] | Distinction between benign and malignant intraductal proliferations of the breast | |
2015 | Veta M. [78] | Mitosis detection in breast cancer | |
2013 | Cireşan D.C. [79] | Mitosis detection in breast cancer | |
2018 | Couture H.D. [80] | Prediction of breast cancer grade | |
2018 | Sahiner B. [81] | Application to Ki67 staining | |
2019 | Hossain M.S. [82] | Automatic quantification of HER2 gene amplification | |
Urology | 2021 | Kott O. [83] | Diagnosis of prostate cancer and Gleason grading |
2020 | Baessler B. [84] | Detection of metastatic testicular germ cell tumors | |
Obstetrics and Gynecology | 2015 | Idowu I. [85] | Detection of true labor and diagnosis of premature labor |
2013 | Manna C. [86] | Identification of most viable oocytes and embryos | |
2019 | Zhang L. [87] | Diagnosis of ovarian tumor | |
2020 | Hart G. [88] | Early detection of endometrial cancer |
Examples of AI Systems Applications in Pathology |
---|
1. Differentiate between benign and malignant tumors |
2. Grading of dysplasia and in situ lesions [70] |
3. Metastasis and micrometastasis detection [74] |
4. Relationships between different immune cell populations [70,71] |
5. IHC/ISH scoring of multiple biomarkers and topography of the immune response [72] |
6. Mitosis detection [78,79] |
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Poalelungi, D.G.; Musat, C.L.; Fulga, A.; Neagu, M.; Neagu, A.I.; Piraianu, A.I.; Fulga, I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J. Pers. Med. 2023, 13, 1214. https://doi.org/10.3390/jpm13081214
Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. Journal of Personalized Medicine. 2023; 13(8):1214. https://doi.org/10.3390/jpm13081214
Chicago/Turabian StylePoalelungi, Diana Gina, Carmina Liana Musat, Ana Fulga, Marius Neagu, Anca Iulia Neagu, Alin Ionut Piraianu, and Iuliu Fulga. 2023. "Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare" Journal of Personalized Medicine 13, no. 8: 1214. https://doi.org/10.3390/jpm13081214