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When Michael Deater’s health worsened while waiting to get a lung transplant at the University of Florida Health Centre, a dedicated team of doctors and nurses attended to him on the primary layer of care.
Following that, sensors and cameras tracked his every move in a smart intensive care unit from vital signs to facial expressions. More than 350 gigabytes of information per patient goes into a central computer where artificial intelligence (AI) then processes the information, assisting physicians to decipher complex emotions and guide care.
This type of AI application has begun to permeate healthcare delivery in many parts of the world and is on track to prevail in a new order. AI might soon be able to provide real-time health recommendations, taking the burden off physicians and providing more time for patients.
It is being deployed to expand the power of radiology and medical imaging due to its capacity to assess massive records and make massive decisions. It can triage, find things early, and find things in large quantities where it might take human days to be able to find the diagnosis.
AI is believed to hold enormous potential for improving health, including improved diagnosis and clinical care, enhanced research and development, disease surveillance, outbreak response, and managing health systems, especially in low and middle-income countries.
For example, an AI-based tool is already being tested to screen for cervical cancer in India, Kenya, Malawi, Rwanda, South Africa, and Zambia, Tedros Ghebreyesus, director-general at the World Health Organization (WHO), said in an address on AI application in healthcare. AI could also be used to improve the detection of tuberculosis, COVID-19, and many other conditions.
The health challenges facing African countries including Nigeria today are familiar pandemics, aging populations, pressure on health systems, access limitations, or skill shortages in remote areas; trying to address these issues with the same old methods of research diagnosis and clinical care is not enough.
“The use of artificial intelligence can relieve pressure on these health system challenges by enabling processes to make sense of big data in rapid time, connecting people to critical information, accelerating outcomes, and delivering breakthroughs. But it has proven across so many industries including science and medicine that AI can make light work of burdensome human activities,” Ghebreyesus said.
The WHO defines AI as the performance by computer programs of tasks that are commonly associated with intelligent beings.
The basis of AI is algorithms that are translated into computer code that carries instructions for rapid analysis and transformation of data into conclusions.
Tech leaders like Bill Gates see AI helping to reduce health inequities dominant in poor countries. The Gates Foundation is now prioritising AI to make sure the tools are used for the health problems that affect the poorest people in the world. He expects that healthcare workers would maximise their time by having AI handle certain tasks like filing insurance claims, dealing with paperwork, and drafting notes from a doctor’s visit.
In his latest memoir, Gates said the technology will accelerate the rate of medical breakthroughs, using large data that is hard for humans to keep track of all the ways that complex biological systems work. More software that can look at data, infer what the pathways are, search for targets on pathogens, and design drugs accordingly are going to spring up, just as some companies are working on cancer drugs that were developed this way.
“Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children,” he wrote in a memo.
Navid Toosi, a biomedical engineer and medical device developer, said physicians can now personalise the delivery of care, making hospitals more efficient and improving access to healthcare by providing accurate decision-making tools.
AI models are helping doctors learn from patients with similar conditions or genetic information and make highly informed decisions about the diagnosis and treatment options. For instance, with cancer disease whose diagnosis can be immensely complicated, AI models can help streamline this process by taking information from a number of sources. This involves feeding an AI model data from the patient’s blood test, X-ray images of the suspected lesions as well as genetic information from a tissue biopsy, Toosi said during a live session on AI.
The trained model can rapidly consolidate this information and provide highly accurate predictions of the patient’s diagnosis, treatment options most likely to succeed as well as the prognosis. The model can understand whether a certain population is more susceptible to a certain disease and whether they would respond more favourably to certain healthcare interventions.
“AI is giving us the stability to have a much more refined and detailed understanding of human health than we’ve ever had before,” Toosi said.
For Nigeria, analysts say the country will need to build a warehouse of data to enjoy the interoperability and the connectivity that can allow the health system to harness the real effectiveness of AI.
Adeola Alli, chief executive of OneHealth, said AI is important but maybe not the core problem that needs to be solved. “It requires data mining and infrastructures that allow medical experts to learn from patterns to make informed decisions.”
A bigger worry for stakeholders like Toosi is that existing regulatory frameworks aren’t designed for AI software intended for diagnosing, treating or managing the disease. They are designed for physical medical devices like surgical implants or more software that have the same output every time that the patient or clinicians are using them traditionally.
They are static in the sense that the developers release a version of the software and no matter how many times it’s used, it will always have the same output for the same data.
On the other hand, AI software behaves completely differently from most software in healthcare because of the intrinsic ability to learn and evolve, ideally becoming more intelligent as suited to the environment in which they are being used.
Gates shares similar thoughts. He states that AIs have to be tested very carefully and properly regulated, which means it will take longer for them to be adopted than in other areas. According to him, governments and philanthropy should create incentives for companies to share AI-generated insights.
Following that, sensors and cameras tracked his every move in a smart intensive care unit from vital signs to facial expressions. More than 350 gigabytes of information per patient goes into a central computer where artificial intelligence (AI) then processes the information, assisting physicians to decipher complex emotions and guide care.
This type of AI application has begun to permeate healthcare delivery in many parts of the world and is on track to prevail in a new order. AI might soon be able to provide real-time health recommendations, taking the burden off physicians and providing more time for patients.
It is being deployed to expand the power of radiology and medical imaging due to its capacity to assess massive records and make massive decisions. It can triage, find things early, and find things in large quantities where it might take human days to be able to find the diagnosis.
AI is believed to hold enormous potential for improving health, including improved diagnosis and clinical care, enhanced research and development, disease surveillance, outbreak response, and managing health systems, especially in low and middle-income countries.
For example, an AI-based tool is already being tested to screen for cervical cancer in India, Kenya, Malawi, Rwanda, South Africa, and Zambia, Tedros Ghebreyesus, director-general at the World Health Organization (WHO), said in an address on AI application in healthcare. AI could also be used to improve the detection of tuberculosis, COVID-19, and many other conditions.
The health challenges facing African countries including Nigeria today are familiar pandemics, aging populations, pressure on health systems, access limitations, or skill shortages in remote areas; trying to address these issues with the same old methods of research diagnosis and clinical care is not enough.
“The use of artificial intelligence can relieve pressure on these health system challenges by enabling processes to make sense of big data in rapid time, connecting people to critical information, accelerating outcomes, and delivering breakthroughs. But it has proven across so many industries including science and medicine that AI can make light work of burdensome human activities,” Ghebreyesus said.
The WHO defines AI as the performance by computer programs of tasks that are commonly associated with intelligent beings.
The basis of AI is algorithms that are translated into computer code that carries instructions for rapid analysis and transformation of data into conclusions.
Tech leaders like Bill Gates see AI helping to reduce health inequities dominant in poor countries. The Gates Foundation is now prioritising AI to make sure the tools are used for the health problems that affect the poorest people in the world. He expects that healthcare workers would maximise their time by having AI handle certain tasks like filing insurance claims, dealing with paperwork, and drafting notes from a doctor’s visit.
In his latest memoir, Gates said the technology will accelerate the rate of medical breakthroughs, using large data that is hard for humans to keep track of all the ways that complex biological systems work. More software that can look at data, infer what the pathways are, search for targets on pathogens, and design drugs accordingly are going to spring up, just as some companies are working on cancer drugs that were developed this way.
“Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children,” he wrote in a memo.
Navid Toosi, a biomedical engineer and medical device developer, said physicians can now personalise the delivery of care, making hospitals more efficient and improving access to healthcare by providing accurate decision-making tools.
AI models are helping doctors learn from patients with similar conditions or genetic information and make highly informed decisions about the diagnosis and treatment options. For instance, with cancer disease whose diagnosis can be immensely complicated, AI models can help streamline this process by taking information from a number of sources. This involves feeding an AI model data from the patient’s blood test, X-ray images of the suspected lesions as well as genetic information from a tissue biopsy, Toosi said during a live session on AI.
The trained model can rapidly consolidate this information and provide highly accurate predictions of the patient’s diagnosis, treatment options most likely to succeed as well as the prognosis. The model can understand whether a certain population is more susceptible to a certain disease and whether they would respond more favourably to certain healthcare interventions.
“AI is giving us the stability to have a much more refined and detailed understanding of human health than we’ve ever had before,” Toosi said.
For Nigeria, analysts say the country will need to build a warehouse of data to enjoy the interoperability and the connectivity that can allow the health system to harness the real effectiveness of AI.
Adeola Alli, chief executive of OneHealth, said AI is important but maybe not the core problem that needs to be solved. “It requires data mining and infrastructures that allow medical experts to learn from patterns to make informed decisions.”
A bigger worry for stakeholders like Toosi is that existing regulatory frameworks aren’t designed for AI software intended for diagnosing, treating or managing the disease. They are designed for physical medical devices like surgical implants or more software that have the same output every time that the patient or clinicians are using them traditionally.
They are static in the sense that the developers release a version of the software and no matter how many times it’s used, it will always have the same output for the same data.
On the other hand, AI software behaves completely differently from most software in healthcare because of the intrinsic ability to learn and evolve, ideally becoming more intelligent as suited to the environment in which they are being used.
Gates shares similar thoughts. He states that AIs have to be tested very carefully and properly regulated, which means it will take longer for them to be adopted than in other areas. According to him, governments and philanthropy should create incentives for companies to share AI-generated insights.
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