Artificial Intelligence (AI) has rapidly transformed various industries, and health care is at the forefront of this revolution. The potential of AI in healthcare spans from enhanced diagnostics to personalized treatment plans and efficient patient management. However, there are important ethical issues to be aware of in conjunction with these breakthroughs. While considering these ethical dilemmas, can AI really transform healthcare?
The potential of AI has generated a lot of interest in its application to healthcare. AI proponents contend that by delivering quicker and more accurate diagnoses, their technology may dramatically improve medical outcomes. AI-driven diagnostic instruments, for instance, have proven to be very accurate in identifying conditions like diabetic retinopathy and breast cancer [1, 2]. Skeptics, on the other hand, express worries about algorithmic bias, data privacy, and the possible dehumanization of patient care in a healthcare system that is driven by technology.
Comprehending the technological progress and ethical ramifications of artificial intelligence in healthcare is essential. This blog examines the potential benefits of AI in healthcare while addressing the opposing viewpoints related to ethical challenges. By evaluating these perspectives, we can better understand how AI can transform healthcare while ensuring patient privacy, safety, and equity.
Improving Diagnostic Accuracy
The most praised use of AI in healthcare is probably how it increases diagnostic accuracy. Diagnoses are made faster and frequently more accurately thanks to machine learning algorithms' superior speed in processing and analyzing enormous volumes of medical data compared to humans. The results of using AI to identify cancer are encouraging. For instance, when it comes to detecting breast cancer from mammograms, Google's AI algorithm has surpassed radiologists [1]. By lowering false positives and negatives, this method facilitates earlier and more precise diagnosis. Comparably, an AI model created by DeepMind has demonstrated astounding precision in identifying more than 50 eye conditions from retinal scans [3].
Figure 1: Development of an AI system to detect cancer in screening mammograms [1]Figure 1: Development of an AI system to detect cancer in screening mammograms [1]
AI is not just used in medicine. AI systems are being utilized in ophthalmology to diagnose illnesses of the retina. An AI system outperformed several ophthalmologists in a research published in The Lancet, demonstrating 94% sensitivity and 98% specificity in the diagnosis of diabetic retinopathy [2]. By offering prompt, precise diagnoses, this technology promises to lessen the strain on healthcare systems, particularly in areas where access to expert treatment is scarce.
Figure 2: AI framework of Retinal Scan [3]
Treatment Plans based on Patient Data
Customized treatment regimes that take into account environmental, lifestyles, and genetic factors can be created because of AI’s ability to assess patient data on an individual basis. AI technologies are being applied at institutions like the National Institutes of Health (NIH) to develop individualized cancer treatment regimens. These instruments evaluate genetic profiles and illness attributes to suggest the best treatments for specific individuals [4]. This tailored method represents a major breakthrough in oncology care, with the goal of minimizing side effects and optimizing treatment outcomes. In cardiology, AI is also involved in patient outcome prediction. Research has indicated that AI can predict recovery durations and post-surgical problems after treatments such as coronary artery bypass grafting [5].
Improving Management and Patient Care
Virtual health assistants and AI-powered monitoring systems are just two examples of how AI is transforming patient care and management in addition to diagnoses and treatment planning. AI chatbots, such as those created by Babylon Health, offer patients round-the-clock assistance by responding to questions about health, classifying symptoms, and providing recommendations [6]. Through prompt interventions and a decrease in the need for frequent doctor visits, these virtual assistants aid in the management of chronic illnesses. By predicting blood sugar changes through the analysis of real-time data from glucose sensors, IBM Watson Health's AI system for managing diabetes enables proactive management of the ailment [7]. By offering ongoing monitoring and tailored care suggestions, these systems can greatly enhance the quality of life for individuals suffering from chronic illnesses.
Drug Detection and Development
AI is speeding up the drug detection process, which has historically been expensive and time-consuming. Artificial intelligence (AI) algorithms are far faster than traditional techniques for identifying possible medication candidates and estimating their efficacy. Insilico Medicine, a pharmaceutical company, used artificial intelligence (AI) to find a new treatment candidate for fibrosis in 46 days, compared to the usual years it takes [8]. This quick identification can expedite the creation of novel therapies as well as speed up the release of essential medications. The efficiency of drug development has been greatly increased by MIT researchers who have created an AI model that can anticipate the molecular structures of novel therapeutic molecules [9]. This model facilitates the development of efficient medications by helping researchers comprehend how various chemicals will interact with biological targets.
Ethical Limitations and Challenges
Although AI has a lot of potential, there are serious ethical and practical issues when it comes to its application in healthcare. Large volumes of data, frequently containing sensitive patient information, are needed for AI systems [10]. It is crucial to protect the security and privacy of this data. The World Health Organization (WHO) and other ethical standards stress the significance of safeguarding patient data and making sure AI systems are accountable and transparent [11]. Another serious issue is algorithmic prejudice. Unfair treatment outcomes may result from AI systems that have been trained on biased data, which can reinforce and even magnify these biases. The necessity of representative and diverse datasets for AI algorithm training in order to reduce this risk is covered in a paper published in the Journal of Medical Ethics [12].
To tackle these ethical dilemmas, continuous efforts are needed to enhance data security, guarantee openness, and reduce prejudice. We can minimize the risks and enhance the advantages of AI in healthcare by addressing these concerns head-on.
Conclusion
AI integration in healthcare is indeed a double-edged sword, presenting both new ethical issues and amazing achievements. On the one hand, AI has shown that it can expedite drug discovery, enhance diagnostic accuracy, and tailor treatments, promising a more efficient and effective healthcare system. On the other hand, significant challenges related to data privacy, algorithmic bias, and the potential dehumanization of patient care must be addressed.
By approaching the integration of AI in healthcare with a balanced perspective, embracing technological advancements while carefully managing ethical and practical challenges, the healthcare sector can leverage AI to improve patient outcomes and provide more effective care. The future of AI in healthcare is promising, but it requires continuous effort to ensure that its benefits are realized in a fair and equitable manner.
References
[1] “International evaluation of an AI system for breast cancer screening | Nature.” Accessed: Jul. 03, 2024. [Online]. Available: https://www-nature-com.colorado.idm.oclc.org/articles/s41586-019-1799-6 https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002689
[2] M. Abràmoff, P. Lavin, M. Birch, N. Shah, and J. Folk, “Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices,” presented at the Npj digital medicine, Springer Nature, Jan. 2018, pp. 1–8. Accessed: Jul. 03, 2024. [Online]. Available: https://research.ebsco.com/linkprocessor/plink?id=24709d47-9249-314b-b3d2-581a5d08145f
[3] “Clinically applicable deep learning for diagnosis and referral in retinal disease | Nature Medicine.” Accessed: Jul. 03, 2024. [Online]. Available: https://www-nature-com.colorado.idm.oclc.org/articles/s41591-018-0107-6
[5] “Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting - The Annals of Thoracic Surgery.” Accessed: Jul. 03, 2024. [Online]. Available: https://www.annalsthoracicsurgery.org/article/S0003-4975(21)01649-0/fulltext
[6] “Babylon Health launches digital assistant for COVID-19,” pharmaphorum. Accessed: Jul. 03, 2024. [Online]. Available: https://pharmaphorum.com/news/babylon-health-launches-digital-assistant-for-covid-19
[7] “Artificial Intelligence to help in the fight against diabetes,” IBM A/NZ Blog. Accessed: Jul. 03, 2024. [Online]. Available: https://www.ibm.com/blogs/ibm-anz/artificial-intelligence-to-help-in-the-fight-against-diabetes/
[8] “Deep learning enables rapid identification of potent DDR1 kinase inhibitors | Nature Biotechnology.” Accessed: Jul. 03, 2024. [Online]. Available: https://www-nature-com.colorado.idm.oclc.org/articles/s41587-019-0224-x
[9] “Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii | Nature Chemical Biology.” Accessed: Jul. 03, 2024. [Online]. Available: https://www-nature-com.colorado.idm.oclc.org/articles/s41589-023-01349-8
[11] “Ethics and governance of artificial intelligence for health.” Accessed: Jul. 03, 2024. [Online]. Available: https://www.who.int/publications/i/item/9789240029200
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