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Note. This is updated from a previous blog post I published a long time ago in robotic healthcare on November 4,2018
In this century of technological advancement, there has been much hype over the recent emerging field of artificial intelligence (AI), defined as the intelligence applied by computational means instead of the natural world, i.e. humans.
AI has gained popularity following innovative applications in fields such as the automotive, finance, military, and healthcare industry. Recently, attention on AI has skyrocketed with the introduction of the ChatGPT chatbot by Microsoft Open AI to help with search engine as well as the use of Google’s DeepMind Alphafold to help in predicting protein structure efforts.
However, as with any emerging technology, ethical and controversial issues arise as explained in my next blog discussing the ability of AI to discriminate against minorities, be malicious, and spread scientific misinformation. Questions over whether artificial intelligence will “take over the world” by, for example, replacing industry sectors with robotics or the uncontrolled use of AI for military purposes are hot topics of debate.
The Media, literature, and particularly the film industry, with movies such as “I, Robot” and “The Terminator”, have certainly expanded our imaginations as to the potential negatives in the field.
Adding fuel to the fire, recent comments from Tesla and SpaceX CEO Elon Musk stating that “A.I. is far more dangerous than nukes” and thus need to be proactively regulated ignite reasonable worries over the use of AI applications.
In healthcare and medical research, however, far from robots replacing human physicians in the foreseeable future, AI devices have been helping physicians and scientists save lives and develop new medical treatments.
Introducing Adam and Eve
A shift in the use of AI in medical research occurred on 12 June 2007 with Adam, a scientific robot developed by researchers in the UK universities of Aberystwyth and Cambridge, able to produce hypotheses about which genes provide information to develop key enzymes able to speed up (catalyse) reactions in the Brewer’s yeast Saccharomyces cerevisiae and experimentally test them robotically. Researchers then individually tested Adam’s hypotheses about the role of 19 genes and discovered that 9 were new and accurate while only 1 was incorrect.
Adam set the precedent for the team to develop a more advanced scientific robot called Eve, which helped identify triclosan, an ingredient found in toothpaste, as a potential anti-malarial drug against drug-resistant malaria parasites which contribute to an estimated malaria mortality of 1.2 million annually.
Eve screened thousands of compounds against specific yeast strains that had their essential genes for growth replaced with equivalent ones either from malaria parasites or humans to find compounds that decreased or stopped the growth of strains dependent on malaria genes but not human genes ( to avoid human toxicity). As a result, triclosan was identified to halt the activity of the DHFR enzyme necessary for malaria survival even in pyrimethamine drug-resistant malaria strains.
Without Eve, it is likely that the research would have still been in progress at this stage and taken years to arrive at the published result, which is what usually happens in the drug discovery field.
To make a drug, on average it takes at least 10 years of arduous research and an estimate of US $2.6 billion with a high percentage of this money spent on drug therapies that fail. AI has the potential to lessen these time, money, and research inefficiency factors.
In the clinic, AI tools can use algorithms to assist physicians with the high volume of patient data, provide updated medical information, reduce therapeutic error, and use this information to provide clinical assistance and diagnosis with over 90% accuracy. The depicted diagram below provides some insight into the structural function of AI and examples of applications in medicine based on the detailed published information found in Jiang et al.
Just as advantages, applying AI in healthcare rings the alarm for ethical issues and analytical concerns which will be discussed in future posts.
However, far being from robotic disaster, AI has proved valuable for the development of human medicine and healthAs Suchi Saria, a professor of computer science and director of the Machine Learning and Health Lab at John Hopkins University, explains in her TEDx talk, AI is already saving lives by detecting symptoms 12-24 hours before a doctor could.
AI in Latin America
This is also the case in Latin America, where as mentioned by a recent report written by Economist Impact and supported by Google, AI is forecasted to increase the region’s GDP greater than 5% by 2030. The report titled “Seizing the opportunity the future of AI in latin America” has a special focus on Argentina, Brazil, Chile, Colombia, and Mexico.
Based on the forecast, AI market in health applications is predicted to grow approximately 28% between the years 2019 and 2027. This will be driven by increasing patient demand and the need to improve healthcare quality as well as ease the flow of healthcare systems.
To note is that, as mentioned in this report, 30% of the Latin American population is not able to access healthcare and out of the five countries focused on, all are securing funding to use AI to combat issues like accessibility potentially through digital health, prediction and prevention of blindness, and managing mental health conditions.
In addition, AI is also being used to improve cancer diagnosis in Latin America and the Caribbean. Researchers Liliana Sussman and colleagues report a summary and analysis of the current studies being done to implement AI in cancer diagnosis, treatment, and prognosis in Latin America and the Caribbean in the scientific journal Frontiers in Medical Technology .
They mention how AI can help improve the “insufficient and inefficient” cancer control partly due to socioeconomically discrepancies and improve the prediction of clinical parameters, cancer research and diagnosis, in addition to advancing personalized medicine.
However, the authors also discuss the setbacks of AI use, including the lack of clinical trials and sociodemographic and economic studies which are needed to implement the use of AI in the Latin America’s standard clinical practice.
Ultimately, AI in healthcare undoubtedly sets the precedent for a new future in medicine.
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