Canvas by Kumayl Mehdi

Overview

kumaylmehdi — 2025-03-17 03:16:36 UTC

I chose to research AI integration into the health industry. I chose the healthcare industry due to its impact on individuals' lives and society. AI being integrated into healthcare is interesting to me as it seems like it may have the ability not only to enhance but revolutionize healthcare and the medical field entirely. I will provide examples of AI technology being used in hospitals and the medical field, such examples are a cloud-based AI orchestra or automated image analysis and reporting in a multi-healthcare system. It can process abdominals cts for screening and achieve a turnaround time of 2.8 minutes simplifying the work heavily. Another example is AI integration in radiology with machine/deep learning algorithms being used for image segmentation, diagnostics, and analytics. And finally the usage of AI in ECG (electrocardiography) for diagnosis, risk stratification, and management of cardiovascular diseases. AI can also assist in administrative tasks such as patient scheduling, billing, and managing electronic health records. Additionally, I will explore how AI can help address disparities in healthcare access and outcomes. Understanding these applications can help highlight the benefits and challenges of AI being integrated into health. Alongside these examples I will analyze future trends and potential developments of AI in the future of this industry and the ethical considerations of AI usage in this industry. I will also discuss the broader societal impact of AI in this industry. Throughout this pallet, I aim to provide an overall comprehensive understanding of how AI is shaping the healthcare industry.

Future Trends and Ethical Considerations

kumaylmehdi — 2025-03-17 03:17:55 UTC

AI is currently transforming the healthcare industry and is expected to reduce workflow heavily with technology like machine learning algorithms being used for image segmentation, diagnostics analytics, and much more. In the future, AI will expand itself from its current role of electronic health records, wearables, and will expand and improve the field, but as AI grows ethical concerns will become more relevant. One major concern currently is data privacy, as AI relies heavily on large datasets that contain sensitive information. According to an article published by Pubmed Central, authored by Dariush Farhud, one of the leading professors in human genetics "In healthcare, current laws are not enough to protect an individual’s health data. Clinical data collected by robots can be hacked into and used for malicious purposes that minimize privacy and security. Some social networks gather and store large amounts of users’ data, for instance, individuals’ mental health data, without their consent, which can be helpful in the marketing, advertising, and sales of these companies."(Farhud et al.). This shows that privacy and data a major concerns as data collected by robots can be utilized unethically and or play a security risk as they can also be hacked into. Another major ethical concern is the replacement of doctors and nurses. The article states "Doctors and nurses are expected to provide treatment in an empathetic and compassionate environment, which will significantly affect the healing process of patients. This will not be achieved with robotic physicians and nurses. Patients will lose empathy, kindness, and appropriate behavior when dealing with robotic physicians and nurses because these robots do not possess human attributes such as compassion" (Farhud et al.) This also demonstrates major ethical dilemmas as AI lacks human empathy which is essential for patient care. These concerns highlight that while AI has the potential to greatly enhance the future of healthcare, it needs to have critical ethical considerations so that progress does not come with a huge cost. I believe that AI will revolutionize the future of this industry however these are major ethical issues that come with it that must be solved first.

Reflection

kumaylmehdi — 2025-03-17 03:18:35 UTC

Throughout researching how AI is becoming more and more integrated into healthcare, I learned how AI is not only improving efficiency but also transforming patient care. One challenge I faced was trying to read and understand these medical papers. I had to do a lot of searching and googling definitions to understand the terms I would read in these papers. After understanding these words, I began to see the potential of AI as I read through these articles surprised at how quickly AI can process data and assist in diagnosis. I now have a deepened understanding of AI's potential in the real world through its many applications in the healthcare industry.

Societal Impact

kumaylmehdi — 2025-03-17 03:19:36 UTC

The rise of AI in healthcare will undoubtedly transform the industry, but it will also bring profound societal impacts regarding employment, equity, and bias. AI's ability to handle tasks like diagnostics, treatment planning, and patient monitoring are all impressive but it will raise concerns about job displacement. An article published by Sciencedirect written by researchers in India states "AI, without human judgment, may pose challenges in clinical decision-making processes."(Mondal). This shows that AI can improve efficiency and reduce the need for certain healthcare roles, specifically in diagnostics and administrative jobs. A negative social impact of AI would be the algorithmic bias and its impact on healthcare equity. This article states "Moreover, the potential for bias in AI algorithms introduces ethical challenges that require scrutiny. If not addressed adequately, biases in data or algorithmic decision-making processes could lead to disparities in healthcare outcomes." if AI is not trained on unbiased data, it will produce unequal results, worsening health disparities for underrepresented groups. This is especially troubling in places in which quality care is already extremely limited. The article also reaffirms this with "(mondal). The integration of AI should not inadvertently exacerbate existing inequalities but rather strive to bridge gaps in healthcare access and outcomes." From my perspective, while AI can enhance healthcare, societal impacts such as job displacement, and potential racial disparities becoming more significant are both issues that should be considered first before implementation. AI should support healthcare professionals and improve equity, not replace people and reinforce systemic biases that already exist in the modern world.

AI ECG

kumaylmehdi — 2025-03-17 03:21:47 UTC

AI is enhancing electrocardiograms which record heart electrical activity and detect abnormalities like arrhythmias. Irtt uses machine and deep learning algorithms to analyze ECG patterns surpassing human accuracy. Benefits to this include rapid precise diagnoses and risk prediction. AI also enhances ECG signal quality aiding and early detection of conditions. Challenges involve data quality, algorithm generalizability, and regulatory approvals. While AI does optimize efficiency, there is still a huge need for peer or expert review. Overall AI provides enhanced diagnostics and better ECGs for patients and reduces workflow heavily.

AI Radiology

kumaylmehdi — 2025-03-17 03:22:35 UTC

AI is enhancing radiology image segmentation using deep learning and convolutional neural networks. CNNs excel at pattern recognition and enable precise identification of anatomical structures and abnormalities. This aids radiology as it improves diagnostic accuracy and speed. Challenges of this are data quality and the overall "black box" nature of deep learning raises concerns about decision making. Validating AI models across diverse patient populations and integrating them into workflows also presents future hurdles. Despite all these issues, AI-driven segmentation is a significant advancement in the world of radiology.

AI Image Analysis

kumaylmehdi — 2025-03-17 03:23:46 UTC

AI automates image analysis and reporting via a cloud-based system integrated seamlessly into clinical workflows. It processes CT scans for hepatic steatosis, delivering results within minutes using deep learning for segmentation. It also calculates attenuation values and aids in diagnosis. The technology used is cloud computing, DICOM, and HL7 and the benefits associated are faster reporting, opportunistic screening, and reduced workload. Challenges are snoring algorithm accuracy, network reliability and the cost of cloud-based systems. Data security and monitoring are also very crucial. Overall, a significant advancement despite the costs and maintenance.

References

kumaylmehdi — 2025-03-17 03:28:09 UTC

Najjar, Reabal. "Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging." Diagnostics (Basel), vol. 13, no. 17, 2023, p. 2760.
Martínez-Sellés, Manuel, and Manuel Marina-Breysse. "Current and Future Use of Artificial Intelligence in Electrocardiography." Journal of Cardiovascular Development and Disease, vol. 10, no. 4, 2023, p. 175.
Chatterjee, Neil, et al. "A Cloud-Based System for Automated AI Image Analysis and Reporting." Journal of Imaging Informatics in Medicine, vol. 38, no. 1, 2024, pp. 368-79.
Mondal, Himel, and Shaikat Mondal. "Ethical and Social Issues Related to AI in Healthcare." Methods in Microbiology, vol. 55, 2024, pp. 247-81.
Farhud, Dariush D., and Shaghayegh Zokaei. "Ethical Issues of Artificial Intelligence in Medicine and Healthcare." Iran Journal of Public Health, vol. 50, no. 11, 2021, pp. i-v.

kumaylmehdi — 2025-03-17 03:50:49 UTC

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