Introduction
Artificial intelligence (AI) encompasses a domain of computer science aiming to enhance task performing efficiency by leveraging intelligent machines (Liu et al., 2018). The development of AI can be traced back since the 1950s when a British polymath, Alan Turing, suggested the possibility of machines solving problems using available information and reasoning, similar to humans (Grzybowski et al., 2024). This concept inspired a computer scientist, John McCarthy, to put this theory into action; he collaborated with researchers like Allen Newell and Herbert A. Simon to create computer programs simulating human problem-solving (Xu et al., 2021).
The implementation of AI in healthcare has been a game-changer as it aids in diagnosis, documentation, drug discovery and drug development. It is important to note that the intention of AI in healthcare is to assist physicians in enhancing efficiency rather than replacing them entirely (Bajwa et al.). Despite this, some might argue its advanced development may be a threat (McCallum et al., 2024); if machines can mimic human thoughts, does that mean they can make wrong decisions? Hence, this article aims to delve into how AI works in healthcare, its benefits and potential limitations.
Applications of AI in healthcare
AI represents a collection of different fields, such as machine learning (ML), to bring intelligence to various applications. Putting into perspective, ML, which is further categorised into supervised, unsupervised and reinforcement learning (RL), allows programs to learn through experience (Bajwa et al., 2021). For instance, supervised learning uses labeled data like radiographs of known tumors to identify unknown tumors in new radiographs. It is also applied in molecular biology, whereby databases are used to conduct structural analysis to study drug interaction. Unsupervised learning attempts to identify information without labels, such as analyzing genetic data to discover subtypes of new mutations or diseases. Lastly, RL involves learning through trial and error to develop strategies for optimisation, such as creating tailored treatment plans. An example of AI application in global healthcare is an outbreak risk software created by BlueDot. This application utilizes ML and automated infectious disease surveillance to analyze articles over 65 countries each day, travel itinerary information, flight paths, and climate to forecast future outbreaks (Basu et al., 2020). This played a vital role in mitigating the COVID-19 outbreak.
2.1. Benefits and Limitations
AI has positively impacted the medical field as it enhances accuracy of diagnosis, allows for tailored treatment plans, enables outbreak forecasting to curb disease outbreaks and overall optimises workflow, resources and time management. This alleviates the burden on healthcare professionals by automating repetitive routine tasks, allowing them to focus on patient care and critical decision-making (Broshkov, 2024). However, several challenges may arise, such as additional training for healthcare professionals, adaptation, ensuring consistent reliability, patient hesitancy as well as high initial costs (Broshkov, 2024). This highlights the need for healthcare
professionals to be aware of the current trends on AI and conduct further research on its implementation in medicine.
Conclusion
In essence, AI plays a significant role in augmenting patient care and unveils new opportunities in medical research. Despite this, it is important to contemplate the challenges it poses to enhance AI’s potential in the medical field.
References
Bajwa, J. et al. (2021) ‘Artificial intelligence in healthcare: transforming the practice of medicine’, Future Healthcare Journal, 8(2), pp. e188–e194. Available at:
Basu, K. et al. (2020) ‘Artificial Intelligence: How is It Changing Medical Sciences and Its Future?’, Indian Journal of Dermatology, 65(5), pp. 365–370. Available at: https://doi.org/10.4103/ijd.IJD_421_20.
Broshkov, D. (2024) AI in healthcare: advantages and disadvantages, ZenBit. Available at: https://zenbit.tech/blog/ai-in-healthcare-advantages-and-disadvantages/ (Accessed: 31 July 2024).
Grzybowski, A., Pawlikowska–Łagód, K. and Lambert, W.C. (2024) ‘A History of Artificial Intelligence’, Clinics in Dermatology, 42(3), pp. 221–229. Available at:
Liu, J. et al. (2018) ‘Artificial Intelligence in the 21st Century’, IEEE Access, 6, pp. 34403– 34421. Available at: https://doi.org/10.1109/ACCESS.2018.2819688.
McCallum, S. et al. (2024) What is AI, how does it work and what can it be used for? Available at: https://www.bbc.com/news/technology-65855333 (Accessed: 17 July 2024).
Xu, Y. et al. (2021) ‘Artificial intelligence: A powerful paradigm for scientific research’, The Innovation, 2(4), p. 100179. Available at: https://doi.org/10.1016/j.xinn.2021.100179.
Article by: Sarah Sharul Sham
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