Artificial Intelligence (AI) is a powerful technology that has been transforming various sectors, including healthcare. A recent report projects that the global AI in the healthcare market is expected to grow from $2.1 billion in 2018 to $19.25 billion by 2026. This growth is driven by the increasing demand for better healthcare services, coupled with the need to reduce healthcare costs. In this article, we will explore how AI is being used for global health and its potential in improving healthcare outcomes.
AI and diagnosis
One of the main areas where AI is being used in healthcare is in the field of diagnosis. AI algorithms are trained to analyze medical data, such as images and texts, to detect diseases and provide accurate diagnoses. This is particularly useful in areas where there is a shortage of medical professionals.
Real-life example: The Beijing-based company, Infervision, has developed an AI system that is capable of detecting lung cancer. The system can analyze CT scans of the lungs and identify potential cancerous lesions with high accuracy. In a trial that involved 6,716 cases, the system outperformed four experienced radiologists in terms of accuracy and speed.
AI and drug discovery
Drug discovery is a lengthy and expensive process that is fraught with challenges. However, AI has the potential to speed up the process and improve the success rate of drug development. AI algorithms can analyze vast amounts of data and identify potential drug candidates that are likely to be effective.
Real-life example: The UK-based company, Insilico Medicine, has developed an AI system that is capable of predicting the efficacy of potential drugs for a range of diseases. The system analyzes data from various sources, including clinical trials and scientific literature, to identify potential drug candidates. In a recent study, the system identified six potential drugs for idiopathic pulmonary fibrosis, a disease for which there is currently no cure.
AI and personalized medicine
Personalized medicine is an approach to healthcare that takes into account the unique characteristics of individual patients, such as their genetic makeup, lifestyle, and medical history. AI is being used to analyze patient data and provide personalized treatment recommendations that are tailored to individual needs.
Real-life example: The US-based company, IBM Watson Health, has developed an AI system that is capable of analyzing patient data to provide personalized cancer treatment recommendations. The system analyzes data from genomic sequencing, medical literature, and clinical trials to identify the most effective treatment options for individual patients.
AI and disease surveillance
Disease surveillance is the systematic collection, analysis, and dissemination of data on diseases. AI is being used to analyze data from various sources, such as social media, search engines, and disease registries, to detect disease outbreaks and monitor the spread of diseases.
Real-life example: The US-based company, BlueDot, has developed an AI system that is capable of detecting disease outbreaks by analyzing vast amounts of data from various sources. The system was able to detect the outbreak of COVID-19 in Wuhan, China, nine days before the World Health Organization (WHO) issued an alert.
AI and telemedicine
Telemedicine is the delivery of healthcare services through telecommunication and information technologies. AI is being used to enhance telemedicine services by providing virtual consultations and diagnoses.
Real-life example: The US-based company, Ada Health, has developed an AI-powered chatbot that can provide medical advice based on a patient’s symptoms. The chatbot asks a series of questions to identify the likely cause of symptoms and provides personalized advice on the next steps to take.
Challenges and limitations of AI in healthcare
Despite the potential benefits of AI in healthcare, there are several challenges and limitations that need to be addressed. One of the main challenges is the lack of regulation around the use of AI in healthcare. There is a need for clear guidelines and standards to ensure the safety and effectiveness of AI-powered healthcare services.
Another challenge is the issue of data privacy and security. AI algorithms require access to vast amounts of patient data to function effectively, and there is a risk that this data could be misused or compromised.
Finally, AI-powered healthcare services are not accessible to everyone. There is a risk that AI could exacerbate existing health inequalities if it is only available to those who can afford it.
Conclusion
AI has the potential to revolutionize healthcare by providing more accurate diagnoses, faster drug discovery, and personalized treatment recommendations. However, there are several challenges and limitations that need to be addressed, including the lack of regulation, data privacy and security concerns, and the risk of exacerbating health inequalities. To ensure that AI is used ethically and responsibly in healthcare, there is a need for clear guidelines and standards, as well as a focus on ensuring that AI-powered healthcare services are accessible to all.