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Artificial Intelligence in Medical Diagnosis
Published:
27 March 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress USEDAT.NET: USA-Europe Data Analysis Trends & Complex Networks Mini Congress Series, Coruña, SP-Miami, USA, 2023
https://doi.org/10.3390/mol2net-09-14286
(registering DOI)
Abstract:
Artificial intelligence (AI) has the potential to revolutionize the domain of medicine, particularly in the realm of medical diagnosis. AI-based diagnostic tools have the ability to analyze large amounts of data and undercover complex patterns that may be hard for humans to detect. Also, it helps to assist healthcare providers to make more precise and prompt diagnoses. This review explores the role of AI in improving medical diagnoses, the limitations associated with this technology, and relevant examples.
Keywords: Artificial intelligence, deep learning, medicine, diagnostic, AI limitations, healthcare
Comments on this paper
Ajit Singh
27 December 2023
The research paper titled "Artificial Intelligence in Medical Diagnosis" provides a concise yet impactful exploration into the transformative potential of artificial intelligence (AI) in the field of medical diagnosis. The abstract effectively communicates the overarching theme, emphasizing AI's capacity to revolutionize medicine, particularly in enhancing the diagnostic process.
The emphasis on AI's role in facilitating healthcare providers to make more precise and prompt diagnoses is a key contribution. By addressing the practical implications of AI in clinical settings, the paper aligns with the broader trend of technology integration for improved patient outcomes.
The abstract's promise to delve into relevant examples is enticing, as real-world applications can often illustrate the tangible impact of AI on medical diagnoses. To enhance the paper, providing specific case studies or examples would strengthen the argument and make the review more engaging for a diverse audience.
The emphasis on AI's role in facilitating healthcare providers to make more precise and prompt diagnoses is a key contribution. By addressing the practical implications of AI in clinical settings, the paper aligns with the broader trend of technology integration for improved patient outcomes.
The abstract's promise to delve into relevant examples is enticing, as real-world applications can often illustrate the tangible impact of AI on medical diagnoses. To enhance the paper, providing specific case studies or examples would strengthen the argument and make the review more engaging for a diverse audience.
Andrea Ruiz-Escudero
10 January 2024
Thank you for your thoughtful feedback! I appreciate your suggestion to include specific case studies or examples. I agree with this recommendation, incorporating real-world applications would not only illustrate the tangible impact of AI on medical diagnoses but also provide a more comprehensive understanding of the practical implications in clinical settings.
Humbert G. Díaz
1 January 2024
Dear author(s), Happy New Year 24, Thank you for your contribution to our conference!!!
We have a question for you, you can read and answer bellow.
Question for Authors:
Which ones could be the SWOTs of integrting early pre-clinical data with clinical data and epidemiological on Artificial Intelligence / Machine Learning (AI/ML) studies in this area?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
We have a question for you, you can read and answer bellow.
Question for Authors:
Which ones could be the SWOTs of integrting early pre-clinical data with clinical data and epidemiological on Artificial Intelligence / Machine Learning (AI/ML) studies in this area?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
Andrea Ruiz-Escudero
10 January 2024
The integration of early preclinical data with clinical and epidemiological data in AI/ML studies in the healthcare industry presents a variety of Strengths, Weaknesses, Opportunities, and Threats (SWOT). The ability to improve the accuracy of AI/ML models by combining comprehensive information from the early stages with larger clinical data is one of the strengths. This combination could lead to earlier pattern recognition and better clinical decision making. Moving toward more efficient and tailored health systems, as well as supporting interdisciplinary research, are examples of opportunities. However, limitations may occur as a result of the diversity and quality of preclinical data, affecting the robustness of the models. Furthermore, a lack of consistency and coordination between multiple data sources could compromise the conclusions' validity and generalizability. Addressing these issues is essential to ensure success and ethics in implementing AI/ML in healthcare.
Benjamin Villa
5 January 2024
Dear Author,
Happy 2024!
What are the possible advantages and disadvantages of using AI in medical diagnoses?
Happy 2024!
What are the possible advantages and disadvantages of using AI in medical diagnoses?
Andrea Ruiz-Escudero
10 January 2024
Thank you for your valuable feedback and happy 2024!
AI has substantial advantages in the realm of medical diagnostics, particularly in the interpretation of medical pictures utilizing deep learning techniques. It is critical, however, to remember that the usefulness of AI is dependent on decisions made by different people in the health system. To realize its full potential, structural concerns affecting the quality of data used to train AI models must be addressed, as well as testing and validation procedures improved. Coordination among all stakeholders is required to successfully incorporate AI into health systems, overcome obstacles, and support the growth of learning-based health systems. Furthermore, the use of AI-based systems creates ethical and privacy problems, which must be addressed through system transparency, security, and explainability. Regardless of their potential benefits, the need for more large-scale clinical studies to assess the accuracy of AI systems emphasizes the need of addressing obstacles and possibilities in specific sectors, such as colorectal cancer screening and multiple sclerosis imaging.
AI has substantial advantages in the realm of medical diagnostics, particularly in the interpretation of medical pictures utilizing deep learning techniques. It is critical, however, to remember that the usefulness of AI is dependent on decisions made by different people in the health system. To realize its full potential, structural concerns affecting the quality of data used to train AI models must be addressed, as well as testing and validation procedures improved. Coordination among all stakeholders is required to successfully incorporate AI into health systems, overcome obstacles, and support the growth of learning-based health systems. Furthermore, the use of AI-based systems creates ethical and privacy problems, which must be addressed through system transparency, security, and explainability. Regardless of their potential benefits, the need for more large-scale clinical studies to assess the accuracy of AI systems emphasizes the need of addressing obstacles and possibilities in specific sectors, such as colorectal cancer screening and multiple sclerosis imaging.
