Introduction
Artificial intelligence’s (AI) meteoric rise has already had far-reaching effects in a number of sectors, one of the most promising being healthcare. Researchers, companies, and investors are all paying close attention to artificial intelligence because of its potentially transformative effects on medical diagnosis, treatment, and patient care (1). To properly utilize AI, however, knowledge of the convergence of health, software engineering, and ethics is essential. This article delves into the interdisciplinary nature of AI in healthcare, addresses the inherent ethical challenges, and illustrates these concepts with real-world instances.
What Software Engineers and Doctors Can Do Together
Experts in the medical field can help determine which applications of artificial intelligence will have the most impact. Their expertise informs the development of AI tools for use in healthcare settings, where they face particular problems and requirements. But, software programmers are responsible for creating the algorithms that run AI apps and turn raw medical data into useful insights (2). Effective and efficient AI-powered tools in the healthcare setting can only be developed through collaboration between these two domains.
Problems and Prospects in the Area of Ethics
There is a special set of ethical concerns and difficulties associated with using AI in healthcare. Patients and healthcare professionals alike stand to lose a great deal if sensitive information is compromised (3). In addition, it is important to recognize and address the problem of algorithmic bias, the unintentional use of AI models that contributes to or exacerbates healthcare inequalities (4). Another worry is that AI could one day replace human doctors, which would make it much more important to find a happy medium between human knowledge and machine learning.
Some Real-World Applications of AI in Healthcare and Their Moral Consequences
Some AI-powered healthcare applications are already improving patient outcomes. Algorithms powered by artificial intelligence have been created to spot the early stages of Alzheimer’s disease in brain scans (6) and diagnose diabetic retinopathy in retinal images (5). (6). These innovations may enhance the precision of diagnoses and pave the way for early interventions, ultimately leading to better health outcomes for patients.
However there are ethical questions that can arise from using these applications. For instance, the data used to train AI models might affect how well the models perform in a certain task, such as disease detection. The effectiveness of an AI model for underrepresented groups may suffer if the training data is not diverse or is biased towards specific demographics (7). Getting rid of these prejudices calls for an in-depth knowledge of the facts and a dedication to developing AI in an ethical manner.
Constructing a Plan for the Moral Use of AI in Medicine
Ethical concerns have been raised about the use of AI in healthcare, highlighting the need for a thorough framework for the ethical use of AI. Data privacy and security, algorithmic openness, fairness, and the necessity of human-AI collaboration are all factors that should be taken into account by this framework (8). By following such a paradigm, healthcare providers may guarantee ethical AI research that takes into account patients’ beliefs and rights while maximizing the benefits of AI.
Conclusion
Responsible and ethical development of AI in healthcare requires an understanding of the complex convergence of medicine, software engineering, and ethics. By resolving these issues and encouraging cross-disciplinary cooperation, we can unlock AI’s full potential and enhance patient care and health outcomes. The healthcare community must remain attentive and put ethical issues first as AI continues to develop to guarantee that these potent tools are used fairly and ethically.
References
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(2) Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://www.nature.com/articles/s41591-018-0300-7
(3) Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://www.nature.com/articles/s41591-018-0272-7
(4) Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544-1547. https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2696739
(5) Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410. https://jamanetwork.com/journals/jama/fullarticle/2588763
(6) Ding, Y., Sohn, J. H., Kawczynski, M. G., Trivedi, H., Harnish, R., Jenkins, N. W., … & Langlotz, C. P. (2019). A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology, 290(2), 456-464. https://pubs.rsna.org/doi/10.1148/radiol.2018180958
(7) Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://science.sciencemag.org/content/366/6464/447
(8) Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: an initial review of publicly available AI ethics tools, methods, and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141-2168. https://link.springer.com/article/10.1007/s11948-019-00165-5