The Power of Artificial Intelligence for Improved Patient Outcomes, Ethical Practices and Overcoming Challenges

Abstract

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

Introduction

Artificial Intelligence (AI) is being used in healthcare to improve patient outcomes, reduce costs, and enhance the efficiency of medical professionals. AI can be used for a variety of tasks such as diagnosing diseases, creating personalized treatment plans, and predicting patient survival rates. Some of the most common forms of AI used within health care include Machine Learning (ML), deep learning, Neural Language Processing (NLP), and Robotic Process Automation (RPA) [11Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z. PMID: 37740191; PMCID: PMC10517477.,22AI in Health Care: Applications, Benefits, and Examples. 2024.].

AI has the potential to revolutionize healthcare by providing new ways to diagnose, treat, and prevent diseases. For example, it can help doctors identify early signs of diseases such as cancer by analyzing medical images. Also helps doctors create personalized treatment plans for patients based on their medical history and other factors [11Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z. PMID: 37740191; PMCID: PMC10517477.]. Additionally, AI can help predict patient survival rates by analyzing large amounts of data from electronic health records. It is important to note that the use of AI in healthcare also raises ethical concerns. The World Health Organization (WHO) has issued guidelines for the ethical use of AI in healthcare. These guidelines emphasize the importance of ensuring that AI is designed and used in a way that respects human rights and promotes equity [33Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021 Feb 15;22(1):14. doi: 10.1186/s12910-021-00577-8. PMID: 33588803; PMCID: PMC7885243.,44Li F, Ruijs N, Lu Y. Ethics & AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare. AI. 2023;4(1):28-53. https://doi.org/10.3390/ai4010003.]. AI has the potential to transform healthcare by improving patient outcomes, reducing costs, and enhancing the efficiency of medical professionals. However, it is important to ensure that AI is used ethically and responsibly to maximize its benefits while minimizing its risks [11Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z. PMID: 37740191; PMCID: PMC10517477.]. Artificial intelligence in health care is a term used to describe the use of machine-learning algorithms and software, to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease [22AI in Health Care: Applications, Benefits, and Examples. 2024.].

Discussion

AI has many applications and benefits in health care, such as enhancing clinical decision-making, improving patient outcomes, reducing costs, increasing efficiency, and enhancing the efficiency of medical professionals. Some of the common types of AI used in health care are machine learning, deep learning, natural language processing, and robotic process automation [11Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z. PMID: 37740191; PMCID: PMC10517477.].

AI is expected to grow significantly in health care in the next decade, as more data becomes available, and more algorithms are developed. However, there are also some challenges and ethical issues related to AI in health care, such as data privacy, algorithmic bias, human oversight, and social impact. Therefore, it is important to ensure that AI is used responsibly and transparently in health care, to improve human health and well-being [55Crook A. Arguing the Pros and Cons of Artificial Intelligence in Healthcare. 2023. https://healthitanalytics.com/news.].

Today, AI is transforming healthcare, finance, and transportation, among other fields, and its impact is only set to grow. In academia, AI has been used to develop intelligent tutoring systems, which are computer programs that can adapt to the needs of individual students [55Crook A. Arguing the Pros and Cons of Artificial Intelligence in Healthcare. 2023. https://healthitanalytics.com/news.]. These systems have improved student learning outcomes in various subjects, including math and science. In research, AI has been used to analyze large datasets and identify patterns that would be difficult for humans to detect; this has led to breakthroughs in fields such as genomics and drug discovery [66Risks and benefits of an AI revolution in medicine. Harvard Gazette. 2020 Nov. https://news.harvard.edu/gazette/story/2020/11.]. AI has been used in healthcare settings to develop diagnostic tools and personalized treatment plans. As AI continues to evolve, it is crucial to ensure that it is developed responsibly and for the benefit of all [66Risks and benefits of an AI revolution in medicine. Harvard Gazette. 2020 Nov. https://news.harvard.edu/gazette/story/2020/11.].

AI is becoming an increasingly important tool in healthcare, with many organizations recognizing its potential to improve patient outcomes and enhance the efficiency of medical professionals.

According to a report by Statista, the use of (AI) in healthcare is on the rise. In 2021, 41% of healthcare leaders in the United States reported that their AI use was at a fully functional level, while another 26% said their AI system was moderately functional. The report also states that the global market size of AI in healthcare is expected to reach $45.2 billion by 2030. Another survey conducted by Optum found that 83% of healthcare organizations have implemented an AI strategy, while another 15% are planning to develop one [77Stewart C. AI in healthcare - statistics & facts. 2024. https://www.statista.com/topics/10011/ai-in-healthcare/.].

But using Artificial Intelligence (AI) in healthcare faces some challenges for example, who is responsible if an AI makes an incorrect diagnosis? Who owns the data generated by the AI? These are complex issues that need to be addressed to ensure that AI is used ethically and responsibly in healthcare [88Wang L, Ding N, Zuo P, Wang X, Rai BK. Application and Challenges of Artificial Intelligence in Medical Imaging. In: Proceedings of the 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES). IEEE Xplore. 2022; 10059898. https://ieeexplore.ieee.org/document/10059898/.]. It is important to note that these risks do not necessarily outweigh the benefits of using AI in healthcare. We will summarize Some of these issues about risks and pose some ethical issues that need to be considered and addressed by developers, regulators, clinicians, and patients [88Wang L, Ding N, Zuo P, Wang X, Rai BK. Application and Challenges of Artificial Intelligence in Medical Imaging. In: Proceedings of the 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES). IEEE Xplore. 2022; 10059898. https://ieeexplore.ieee.org/document/10059898/.,99Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform. 2020 Jul;24(7):1837-1857. doi: 10.1109/JBHI.2020.2991043. PMID: 32609615; PMCID: PMC8580417.].

−    Errors and patient harm and data quality issues: AI requires large and diverse datasets to train and validate their performance. However, medical imaging data is often limited, noisy, imbalanced, or biased, which can affect the accuracy and generalizability of the models. Data quality issues can also arise from different imaging modalities, protocols, and standards across different institutions and regions [99Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform. 2020 Jul;24(7):1837-1857. doi: 10.1109/JBHI.2020.2991043. PMID: 32609615; PMCID: PMC8580417.]. Also, can sometimes be wrong, leading to patient injury or other healthcare problems. For example, if an AI model and system incorrectly diagnoses a patient with a disease, the patient may receive unnecessary treatment or miss out on necessary treatment [1010Kent J. Over 80% of Health Execs Have Artificial Intelligence Plans in Place. 2020. https://healthitanalytics.com/news.].

−    Risk of bias and increased health inequalities: AI can be biased if they are trained on data that is not representative of the population as a whole [1111Karimian G, Petelos E, Evers SMAA. The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI Ethics. 2022;2:539-551. https://doi.org/10.1007/s43681-021-00131-7.]. This can lead to health inequalities if certain groups are underrepresented in the data used to train the system. It can reflect or amplify the biases and prejudices that exist in the data they are trained on or in the algorithms they use [99Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform. 2020 Jul;24(7):1837-1857. doi: 10.1109/JBHI.2020.2991043. PMID: 32609615; PMCID: PMC8580417.]. This can lead to inaccurate, unfair, or discriminatory outcomes for certain groups of patients, such as those from different races, ethnicities, genders, or socioeconomic backgrounds. Therefore, bias and fairness issues are important to ensure the quality, validity, and equity of AI and to avoid harming or excluding patients [88Wang L, Ding N, Zuo P, Wang X, Rai BK. Application and Challenges of Artificial Intelligence in Medical Imaging. In: Proceedings of the 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES). IEEE Xplore. 2022; 10059898. https://ieeexplore.ieee.org/document/10059898/.].

−    Lack of transparency and trust: AI models and systems can be complex, opaque, or unpredictable in their decision-making processes or outcomes [1212Keeping a human in the loop: Managing the ethics of AI in medicine. ScienceDaily. 2023 Oct. https://www.sciencedaily.com/releases/2023/10/231020105631.htm.]. This can make it difficult for clinicians and patients to understand how or why an AI reached a certain diagnosis, recommendation, or prediction. Therefore, transparency and the ability to explain issues are essential to ensure the accountability, reliability, and trustworthiness of AI and to enable informed consent and shared decision-making by clinicians and patients [1313Busch F, Adams LC, Bressem KK. Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education. Med Sci Educ. 2023;33:1007-1012. https://doi.org/10.1007/s40670-023-01815-x.]. AI can be difficult to understand, making it hard for patients and medical professionals to trust them. This lack of transparency can also make it difficult to identify errors or biases in the system [99Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform. 2020 Jul;24(7):1837-1857. doi: 10.1109/JBHI.2020.2991043. PMID: 32609615; PMCID: PMC8580417.].

−    Vulnerability to hacking and data privacy and security: healthcare data contains sensitive and personal information about patients, such as their identity, health status, and medical history. Therefore, data security issues are crucial to protect the privacy and confidentiality of patients and prevent data breaches or misuse [1414Ethical issues with artificial intelligence and healthcare. 2022.: https://www.immerse.education/study-tips/ethical-issues-with-artificial-intelligence-and-healthcare/.]. Data security issues can also involve ethical and legal aspects, such as data ownership, consent, and sharing [88Wang L, Ding N, Zuo P, Wang X, Rai BK. Application and Challenges of Artificial Intelligence in Medical Imaging. In: Proceedings of the 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES). IEEE Xplore. 2022; 10059898. https://ieeexplore.ieee.org/document/10059898/.]. AI relies on large amounts of data, which can be vulnerable to hacking and data privacy breaches. This can lead to sensitive patient information being exposed or stolen. AI often relies on large amounts of personal and sensitive data from patients, such as their medical records, genetic information, or biometric data [1414Ethical issues with artificial intelligence and healthcare. 2022.: https://www.immerse.education/study-tips/ethical-issues-with-artificial-intelligence-and-healthcare/.]. This data can be vulnerable to hacking, misuse, or unauthorized access by third parties. Therefore, privacy and security issues are crucial to protect the confidentiality and dignity of patients and to ensure their consent and control over their data [1515Artificial intelligence in medicine raises legal and ethical concerns. The Conversation. 2019. https://theconversation.com/artificial-intelligence-in-medicine-raises-legal-and-ethical-concerns-122504.].

−    Legal and ethical concerns during clinical application issues: AI needs to be validated and tested in real-world clinical settings before it can be widely adopted and integrated into the workflow. However, clinical application issues can arise from the lack of interoperability, standardization, and regulation of AI products, as well as the resistance or skepticism of clinicians and patients. Clinical application issues can also involve ethical and social aspects, such as accountability, transparency, explainability, and trust [1515Artificial intelligence in medicine raises legal and ethical concerns. The Conversation. 2019. https://theconversation.com/artificial-intelligence-in-medicine-raises-legal-and-ethical-concerns-122504.].

−    Human oversight and responsibility: AI can augment or replace human roles and functions in health care, such as diagnosis, treatment, or care delivery [1616Meet the opportunity and challenge of AI in medical imaging. TechTarget. https://www.techtarget.com/searchhealthit/podcast/Meet-the-opportunity-and-challenge-of-AI-in-medical-imaging.]. This can raise questions about the appropriate level of human involvement, supervision, or intervention in the use of AI [1212Keeping a human in the loop: Managing the ethics of AI in medicine. ScienceDaily. 2023 Oct. https://www.sciencedaily.com/releases/2023/10/231020105631.htm.]. It can also raise questions about the allocation of responsibility, liability, or blame when something goes wrong with an AI. Therefore, human oversight and responsibility issues are vital to ensure the safety, quality, and ethics of health care and to respect the autonomy, dignity, and rights of clinicians and patients [1717Potential benefits and drawbacks of using AI tools in the field of clinical radiology. 2023. https://www.news-medical.net/news/20230524/Potential-benefits-and-drawbacks-of-using-AI-tools-in-the-field-of-clinical-radiology.aspx.].

Conclusion

The integration of Artificial Intelligence (AI) into healthcare holds immense promise for transforming patient outcomes, reducing costs, and enhancing the efficiency of medical professionals. The applications of AI, ranging from early disease diagnosis to personalized treatment plans, showcase its potential to revolutionize the healthcare landscape. However, as we navigate this transformative journey, it is crucial to remain vigilant and address the ethical considerations and challenges associated with AI implementation.

The ethical use of AI in healthcare, as emphasized by the World Health Organization’s guidelines, demands a careful balance between innovation and responsible practices. The identified challenges, including potential errors, bias, transparency issues, and data privacy concerns, underscore the need for continuous scrutiny and adaptation in the deployment of AI technologies.

As AI in healthcare continues to evolve, stakeholders—developers, regulators, clinicians, and patients—must collaboratively address these challenges. Transparency, accountability, and a commitment to human oversight are vital components of a responsible AI ecosystem. By doing so, we can harness the full potential of AI in healthcare while mitigating risks and ensuring the utmost care for patient well-being and privacy.

In this dynamic landscape, the journey towards a technologically advanced and ethically sound healthcare system requires ongoing dialogue, collaboration, and a commitment to refining AI applications. As we strive for innovation, let ethical considerations and responsible practices guide the path forward, ensuring that the benefits of AI in healthcare are realized while safeguarding the well-being and rights of every individual.

Acknowledgment

The author thanks the journal editors and reviewers for their constructive feedback.

As well as the Stemosis for Scientific Research and the Syrian Professionals Network.

It is worth noting that we use “AI-generated content” in translation and paraphrasing in this research.

References

1. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z. PMID: 37740191; PMCID: PMC10517477.

2. AI in Health Care: Applications, Benefits, and Examples. 2024.

3. Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021 Feb 15;22(1):14. doi: 10.1186/s12910-021-00577-8. PMID: 33588803; PMCID: PMC7885243.

4. Li F, Ruijs N, Lu Y. Ethics & AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare. AI. 2023;4(1):28-53. https://doi.org/10.3390/ai4010003.

5. Crook A. Arguing the Pros and Cons of Artificial Intelligence in Healthcare. 2023. https://healthitanalytics.com/news.

6. Risks and benefits of an AI revolution in medicine. Harvard Gazette. 2020 Nov. https://news.harvard.edu/gazette/story/2020/11.

7. Stewart C. AI in healthcare - statistics & facts. 2024. https://www.statista.com/topics/10011/ai-in-healthcare/.

8. Wang L, Ding N, Zuo P, Wang X, Rai BK. Application and Challenges of Artificial Intelligence in Medical Imaging. In: Proceedings of the 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES). IEEE Xplore. 2022; 10059898. https://ieeexplore.ieee.org/document/10059898/.

9. Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform. 2020 Jul;24(7):1837-1857. doi: 10.1109/JBHI.2020.2991043. PMID: 32609615; PMCID: PMC8580417.

10. Kent J. Over 80% of Health Execs Have Artificial Intelligence Plans in Place. 2020. https://healthitanalytics.com/news.

11. Karimian G, Petelos E, Evers SMAA. The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI Ethics. 2022;2:539-551. https://doi.org/10.1007/s43681-021-00131-7.

12. Keeping a human in the loop: Managing the ethics of AI in medicine. ScienceDaily. 2023 Oct. https://www.sciencedaily.com/releases/2023/10/231020105631.htm.

13. Busch F, Adams LC, Bressem KK. Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education. Med Sci Educ. 2023;33:1007-1012. https://doi.org/10.1007/s40670-023-01815-x.

14. Ethical issues with artificial intelligence and healthcare. 2022.: https://www.immerse.education/study-tips/ethical-issues-with-artificial-intelligence-and-healthcare/.

15. Artificial intelligence in medicine raises legal and ethical concerns. The Conversation. 2019. https://theconversation.com/artificial-intelligence-in-medicine-raises-legal-and-ethical-concerns-122504.

16. Meet the opportunity and challenge of AI in medical imaging. TechTarget. https://www.techtarget.com/searchhealthit/podcast/Meet-the-opportunity-and-challenge-of-AI-in-medical-imaging.

17. Potential benefits and drawbacks of using AI tools in the field of clinical radiology. 2023. https://www.news-medical.net/news/20230524/Potential-benefits-and-drawbacks-of-using-AI-tools-in-the-field-of-clinical-radiology.aspx.

記事について

この記事を引用する

Almasri IA. The Power of Artificial Intelligence for Improved Patient Outcomes, Ethical Practices and Overcoming Challenges. IgMin Res. July 15, 2024; 2(7): 585-588. IgMin ID: igmin222; DOI: 10.61927/igmin222; Available at: igmin.link/p222

• DOI10.61927/igmin222

12 Dec, 2023

受け取った

15 Jul, 2024

受け入れられた

16 Jul, 2024

発行された

この記事を共有する

次のリンクを共有した人は、このコンテンツを読むことができます:

共有リンクを取得する

トピックス

Clinical Medicine