Tanisi – Year 9 Student
Editor’s note: This excellent essay was the winning entry into GSAL’s internal British Science Week Essay Competition, open to all students in years 7-10. According to the judges, Sixth Form student Science Faculty Leaders, “The first essay on AI in medicine effectively uses sources and quotes to eloquently discuss the possible uses of AI in medicine. It was particularly interesting to see just how many contrasting ways AI can be implemented into the healthcare structure. Overall we have decided the winner is [this] essay because of its comprehensive yet condensed overview on AI in medicine that also proved to be very interesting with credible sources.” Congratulations to Tanisi, and well done to all students who took the time to enter this competition. CPD
In 1956, John McArthy was the first to define AI as ‘the science and engineering of making intelligent machines’. Intelligent machines, that may surpass the bounds of human reason, built to aid us, as tools of progression. How can we utilise the extent of these capabilities, and how should we implement this into healthcare? ‘AI is perhaps the most transformational technology of our time, and healthcare is perhaps AI’s most pressing application’ [1], claims Nadella; we have no conclusive evidence which constrains AI’s potential to one distinct role, simply the notion of its malleability.
The current role of AI in healthcare and patient-management, however, is easily palpable. Pattern-recognition is essential in diagnosis; and AI can utilise this using its flowcharts and database approach.
Flow charts are an easy alternative to the clinician’s manual history-taking, with the patient simply having to input their details– faster, and much more efficient. An example of this, is Heidi (implemented predominantly in general practice), a platform which creates medical flowcharts to concise the efforts of history taking, and in turn, aiding the conclusion towards diagnosis.
An individual cannot know all there is, they would have to constantly update their understanding by spending their time immersed in research. That is where databases come in; AI can be fed information, knowledge pooled together from various health institutes. All it has to do is engrain those patterns, those algorithms, and to recognise the link between the symptoms and the diagnosis.
Moreover, diagnosis via AI may increase the probability of detection and accuracy; In 2017, Stanford University published a study which discussed their utilisation of AI to classify skin tumours, by creating labelled picture associations [2]. Artificial intelligence has demonstrated, time and time again, its adaptability to various medicinal disciplines; in fields like dermatology, AI has been able to keep on par with, if not at a higher-level, skilled dermatologists in the classification of skin lesions [3]. It improves efficiency; clinicians aren’t forced to diverge from duties and can focus on the completion of such without the worry of uncompensated labour.
The aforementioned are only in regard to patient-specific care; AI systems don’t have to be confined to menial tasks such as appointment bookings and dates. In 2023, research was conducted to explore the prospect of AI’s potential for the prediction of pancreatic cancer [4], often regarded as one of the more difficult and expensive cancer variants to diagnose.
AI has the potential to play a major role in treatment and medical research, having already aided us in our understanding of detection patterns in various skin diseases; DermaSensor, an AI- powered tool made to diagnose cancers such as melanoma, would have sounded absurd a few decades ago.
Over the last century, AI has grown exponentially, navigating uncharted territory, whilst changing the very foundation of our understanding. In regards to the question, and as demonstrated by my prior examples– artificial intelligence is flexible, will one day be adapted to fit the mould of whatever we deem fit.
Bibliography
[1] Microsoft. (2019). Satya Nadella announces strategic collaboration with Novartis. Retrieved from https://www.youtube.com/watch?v=wMfsQE-D2q4
[2] De, A., Sarda, A., Gupta, S., & Das, S. (2020). Use of artificial intelligence in dermatology. Indian Journal of Dermatology, 65(5), 352. https://doi.org/10.4103/ijd.ijd_418_20
[3] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8. doi: 10.1038/nature21056
[4] Pesheva, E. (2023, May 8). AI Predicts Future Pancreatic Cancer. Retrieved from hms.harvard.edu website: https://hms.harvard.edu/news/ai-predicts-future-pancreatic-cancer
[5] Liao, J., Li, X., Gan, Y., Han, S., Rong, P., Wang, W., Li, W., & Zhou, L. (2023). Artificial intelligence assists precision medicine in cancer treatment. Frontiers in oncology, 12, 998222. https://doi.org/10.3389/fonc.2022.998222
Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of family medicine and primary care, 8(7), 2328–2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19
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