The Next-Level Precision Medicine in Cancer Management Using Artificial Intelligence

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

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Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

Journal of Parkinson s Disease ◽

10.3233/jpd-212545 ◽

2021 ◽

pp. 1-6

Author(s):

Matt Landers ◽

Suchi Saria ◽

Alberto J. Espay

Keyword(s):

Artificial Intelligence ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Precision Medicine ◽

Movement Disorders ◽

Living Environment ◽

Patient Status ◽

Patient Symptom ◽

Molecular Therapies ◽

Improve Patient

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

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Will Artificial Intelligence Deliver Precision Medicine for Patients With Aortic Stenosis?

JACC Cardiovascular Interventions ◽

10.1016/j.jcin.2021.08.046 ◽

2021 ◽

Vol 14 (19) ◽

pp. 2141-2143

Author(s):

Nico Bruining ◽

Peter P.T. de Jaegere

Keyword(s):

Artificial Intelligence ◽

Aortic Stenosis ◽

Precision Medicine

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Research and application of artificial intelligence based on electronic health record data from patients with cancer: a systematic review (Preprint)

10.2196/preprints.33799 ◽

2021 ◽

Author(s):

Xinyu Yang ◽

Dongmei Mu ◽

Hao Peng ◽

Hua Li ◽

Ying Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Electronic Health Records ◽

Ensemble Methods ◽

Future Research ◽

Electronic Health Record Data ◽

Health Records ◽

Patients With Cancer ◽

Cancer Management ◽

Treatment And Prevention ◽

Electronic Health

BACKGROUND With the accumulation of electronic health records data and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of electronic health records and artificial intelligence in cancer care. OBJECTIVE In this study, we reviewed the literature on the application of AI based on EHR data from patients with cancer, hoping to provide reference for subsequent researchers, and help accelerate the application of EHR data and AI technology in the field of cancer, so as to help patients get more scientific and accurate treatment. METHODS Three databases were systematically searched to retrieve potentially relevant articles published from January 2009 to October 2020. A combination of terms related to "electronic health records", "artificial intelligence" and "cancer" was used to search for these publications. RESULTS Of the 1034 articles considered, 148 met the inclusion criteria. The review has shown that ensemble methods and deep learning were on the rise. It presented the representative literatures on the subfield of cancer diagnosis, treatment and care. In addition, the vast majority of studies in this area were based on private institutional databases, resulting in poor portability of the proposed methodology process. CONCLUSIONS The use of new methods and electronic health records data sharing and fusion were recommended for future research. With the help of specialists, artificial intelligence and the mining of massive electronic medical records could provide great opportunities for improving cancer management.

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Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study

Journal of Medical Internet Research ◽

10.2196/15511 ◽

2019 ◽

Vol 21 (11) ◽

pp. e15511 ◽

Cited By ~ 3

Author(s):

Bach Xuan Tran ◽

Son Nghiem ◽

Oz Sahin ◽

Tuan Manh Vu ◽

Giang Hai Ha ◽

...

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Precision Medicine ◽

Data Science ◽

Latent Dirichlet Allocation ◽

Developed Countries ◽

Theory And Practice ◽

Clinical Practices ◽

Research Topics ◽

Dirichlet Allocation

Background Artificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. Objective This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. Methods We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. Results The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. Conclusions The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

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Artificial intelligence-assisted esophageal cancer management: Now and future

World Journal of Gastroenterology ◽

10.3748/wjg.v26.i35.5256 ◽

2020 ◽

Vol 26 (35) ◽

pp. 5256-5271

Author(s):

Yu-Hang Zhang ◽

Lin-Jie Guo ◽

Xiang-Lei Yuan ◽

Bing Hu

Keyword(s):

Artificial Intelligence ◽

Esophageal Cancer ◽

Cancer Management

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Current State of Personalized Genitourinary Cancer Radiotherapy in the Era of Precision Medicine

Frontiers in Oncology ◽

10.3389/fonc.2021.675311 ◽

2021 ◽

Vol 11 ◽

Author(s):

Sophia C. Kamran ◽

Jason A. Efstathiou

Keyword(s):

Radiation Therapy ◽

Precision Medicine ◽

Tumor Biology ◽

Precision Oncology ◽

Cancer Management ◽

Genitourinary Cancer ◽

Current State ◽

Molecular Therapies ◽

Targeted Molecular Therapies ◽

Development And Validation

Radiation therapy plays a crucial role for the management of genitourinary malignancies, with technological advancements that have led to improvements in outcomes and decrease in treatment toxicities. However, better risk-stratification and identification of patients for appropriate treatments is necessary. Recent advancements in imaging and novel genomic techniques can provide additional individualized tumor and patient information to further inform and guide treatment decisions for genitourinary cancer patients. In addition, the development and use of targeted molecular therapies based on tumor biology can result in individualized treatment recommendations. In this review, we discuss the advances in precision oncology techniques along with current applications for personalized genitourinary cancer management. We also highlight the opportunities and challenges when applying precision medicine principles to the field of radiation oncology. The identification, development and validation of biomarkers has the potential to personalize radiation therapy for genitourinary malignancies so that we may improve treatment outcomes, decrease radiation-specific toxicities, and lead to better long-term quality of life for GU cancer survivors.

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The Role of Artificial Intelligence (AI) in Cancer Treatment in Low-Middle Income Countries: The Watson for Oncology (WfO) Experience (Preprint)

10.2196/preprints.31461 ◽

2021 ◽

Author(s):

Angela Rui ◽

Srinivas Emani ◽

Hermano Alexandre Lima Rocha ◽

Rubina F. Rizvi ◽

Sergio Ferreira Juaçaba ◽

...

Keyword(s):

Artificial Intelligence ◽

Cancer Treatment ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Cultural Barriers ◽

Successful Implementation ◽

Middle Income ◽

Middle Income Countries ◽

Cancer Management ◽

Low Middle Income Countries

UNSTRUCTURED As technology continues to improve, healthcare systems have the opportunity to utilize a variety of innovative tools for decision making that extend beyond traditional clinical decision support systems (CDSSs). The feasibility and efficacy integrating artificial intelligence (AI) systems into medical practice has shown variable success, especially in resource-poor areas. In this paper, we cover the existing challenges surrounding cancer treatment in low-middle income countries (LMICs). By focusing on the implementation of an AI-based CDSS for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally. Additionally, we summarize current physician perspectives from China, India, Brazil, Thailand, and Mexico in regard to their experiences and recommendations for improving the system. By doing so, we hope to highlight the need for additional research on user experience and unique cultural barriers for the successful implementation of AI in LMICs.

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