Improving Healthcare with
Machine Intelligence (MI) is already revolutionizing our healthcare. Better diagnosis, improved operational excellence, and faster creation of new drugs are the results of this revolution. While the current major beneficiaries of MI-powered healthcare are developed countries, these technologies can also be applied to enhance healthcare in the developing world.
MI technologies are on the way to becoming an essential part of modern healthcare systems. They assist doctors identifying diseases and find the most suitable treatments, are applied in the development of new medicines by pharma companies, and open new ways for improvement of the health system. In fact, MI technologies are already driving major innovations and operations excellence in the healthcare space: according to an Accenture study, in the United States alone, MI-powered healthcare applications will create $150 billion in annual savings by the year 2026 . Thus, it is not surprisingly that just in 2019 alone venture capitalists have invested around $4 billion in MI-driven healthcare innovations, especially in applications which use Artificial Intelligence (AI) .
While the developed world is on the way to achieving the utopian dream of machines capable of healing humans from (almost) any diseases, many developing countries are struggling to ensure access to basic healthcare for their citizens. For example:
1.4 million people died from tuberculosis in 2019 . Over 95% of tuberculosis cases and deaths were in developing countries.
14.6 % of all babies born globally in 2015 suffered from low birth weight often originating in bad nutrition, inadequate antenatal care, and dirty environment ; developing countries are affected disproportionally strongly by this phenomenon.
One of ten medications in poorer countries are fraudulent or substandard according to data from the World Health Organization (WHO) .
Low birthweight prevalence, by country and region, 2015 .
The good news is that MI can help improve healthcare systems also in the developing world. In the following we present several examples of how MI is facilitating better healthcare. While some of these examples have their roots in the developed countries, and thus may yet not be accessible to the needy in poorer developing countries, NPOs/NGOs who are active in those parts of the world can certainly explore ways to benefit from these types of examples. In addition, we list several use cases for MI applications in healthcare that tackle issues specific for developing countries.
One of the major applications of MI in healthcare is a better identification of diseases, especially, when diagnoses are made on the basis of images.
AI algorithms can be applied in radiology to identify abnormalities via X-ray images. For example, researchers have developed an AI system that can detect pneumonia from chest X-ray imagery . For this purpose, they applied the so-called convolutional neural network – a specific AI algorithm that is suited for processing and classification of images. To train and test the system, researchers have used around 100,000 frontal-view X-ray images of nearly 30,000 unique patients. The resulting prediction accuracy of the system was compared to diagnoses from the same set of images made by four practicing radiologists from Stanford University. The AI system significantly outperformed three of the four radiologists! Only the most experienced radiologist was able to perform a little bit better than the AI system. On an average, this AI system can detect pneumonia from chest X-rays at an accuracy level exceeding that of practicing experts. To be fair, one should mention that in order to make radiologist’s diagnoses comparable with those of AI, radiologists were not permitted to use patient history which can be a very important part of the diagnosis process. However, future AI systems will also incorporate patient history in their predictions so that they will become even more accurate and can be used by practicing radiologists as a tool to provide better diagnoses.
Some results of the AI-powered pneumonia detection system .
Another disease that is primarily diagnosed visually is skin cancer. Researchers from Stanford University have applied similar AI techniques as in the previous example to train AI to identify skin cancer on images . For this purpose, they have used around 130,000 clinical images and compared the performance of the AI system to diagnoses made by more than 20 dermatologists. The system achieved performance on par with all tested experts demonstrating that AI is capable of classifying skin cancer with a level of competence comparable to expert dermatologists. This insight opens exciting new opportunities to extend the reach of dermatologists outside of the clinic: if smartphones are outfitted with an AI system as described above, every smartphone owner can scan her skin to identify potential skin cancer and, in the case of a positive result, contact her doctor promptly. Since many who currently notice an unusual spot on their skin dismiss it as nothing and avoid going to the doctor until it is too late, such skin cancer apps can save many lives! And indeed, there are already such solutions on the market like that from the Dutch company SkinVision . In addition, AI-powered skin cancer identification apps can be applied in developing countries where only a few dermatologists are available or are located far away from places where patients are living. For example, if in an isolated village at least one smartphone with the corresponding app is available, inhabitants can regularly scan their skin for cancer. In this way, a low-cost universal access to vital skin diagnostic care can be provided in developing regions.
Visual identification of diseases is not the only field of medical diagnosis where MI can be applied. A team of Chinese and US researchers have used Machine Learning (ML) for the analysis of diverse and massive amounts of electronic health record data to develop a system that is capable to query electronic health records in a manner similar to the hypothetico-deductive reasoning used by physicians during the diagnosis process . Their system uses ML-based natural language processing techniques to extract clinically relevant information from digital records and was trained on more than 100 million data records of pediatric patients. The system achieved high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. This and similar systems can support physicians in tackling large amounts of data, augmenting diagnostic evaluations, and providing clinical decision support in cases of diagnostic uncertainty or complexity. Particularly, such systems can be extremely valuable for regions where medical experts are in shortage.
The development process for a new medicine is challenging, very time-consuming, and extremely expensive. On an average, it takes 10 to 15 years to develop a new single drug . On the other hand, the average success rate of a drug discovery process is only 10%. These facts lead to extremely high development cost for a single drug with estimated median of $985 million . Altogether it results in a very slow and often unprofitable drug discovery process. Through automated analysis of huge amounts of data and by identifying more promising drug candidates, AI algorithms can help reduce the development costs, increase the success rate, and speed up the development cycle of medicine.
And indeed, there are already drugs which were developed with the help of AI: DSP-1181, a molecule of the drug treating obsessive-compulsive disorder, was invented by AI and accepted for a human trial . The development of this drug took only 12 months. Another example of AI usage in drug development is the discovery of six novel inhibitors of the DDR1 gene, a kinase target implicated in fibrosis and other diseases . To achieve this result, the AI system needed only 21 days! The developed substances were tested on mice and showed positive results.
Also, during the current COVID-19 pandemic AI is being used heavily for research on potential medicine and vaccines . Indeed, coronavirus has demonstrated an essential need in faster development processes for drugs and vaccines since, according to WHO, it will not be the last pandemic due to worsening ecological crises .
Tackling operational and organizational challenges in healthcare
While patient care is the main focus of any healthcare facility, doctors and nurses are often sidetracked from their main activity by the need to handle a lot of administrative tasks and paperwork, e.g., scheduling, claim and bill management, and transferring records from one system to another. These tasks are usually rather simple, digitalized, rule-based, and of high volume – ideal prerequisites to automate these processes using robotic process automation (RPA) (s. e.g.,  or ). RPA is a technology that utilizes “software bots” which can mimic human user interactions with a digital system. It means that an RPA bot is able to perform exactly the same steps on a computer as a doctor or nurse would do them. RPA systems are quite flexible so that any strict rule-based digital process currently performed by a human can be potentially automatized by a bot. Since RPA utilizes software bots, there is no need for any changes in the existing IT landscape of a hospital, thus making the introduction of this innovation very easy and affordable. RPA can also be combined with other MI technologies like AI and ML in order to automize more complex, non-deterministic processes. Altogether, RPA helps free up medical personnel from monotonous paperwork and enables doctors and nurses to focus on what really counts — taking care of the health of their patients.
Another MI technique useful for operational excellence in healthcare facilities is Process Mining . During Process Mining, specialized algorithms are applied to the so-called log data — the data that are collected and stored every time an activity in the IT system is performed. These algorithms identify trends, patterns and details contained in the logs and provide insights regarding process flows, process bottlenecks and potential opportunities for optimization. Process mining enables businesses to improve their process efficiency and can also be applied in healthcare facilities. For example, a team of Dutch researchers has applied Process Mining for the analysis of a gynecological oncology process in a Dutch hospital  which serves as a proof-of-concept for the application of Process Mining in healthcare facilities.
MI healthcare applications in developing countries
MI technologies can also be used to enhance healthcare in developing countries. For example, an Indian social startup Wadhwani AI is using AI to tackle tuberculosis. Tuberculosis hotspots are usually identified by income and population density of a region. However, this evaluation approach is very unprecise. Wadhwani AI has developed a system that has learned more granular characteristics of regions at risk . With this approach it is possible to identify potential tuberculosis hotspots also in non-obvious locations. Currently, Wadhwani is also working on an AI-powered system which can diagnose tuberculosis based on cough sounds. Since such a system can be installed on a smartphone, the identification of potential patients can be drastically accelerated with this approach.
Another problem that Wadhwani AI is tackling using MI technologies is to identify low-birth-weight babies. Low weight of a new-born is a serious problem; if such a baby does not get appropriate care, low birthweight can result in stunted growth, lower IQ, diabetes, or even early death. Unfortunately, in many regions of the world infants are not weighed at birth or are weighed inaccurately. Wadhwani AI is developing an AI-powered smartphone-based anthropometry tool which creates a 3D model of an infant and estimates all relevant anthropometric parameters. Using this system, frontline volunteers can screen for low-birth-weight babies in underprivileged regions without any specific equipment. This system is currently tested in four states in India across diverse settings like rural homes, primary health facilities, and hospitals.
Counterfeit drugs identification system from Veripad and slalom in action .
MI can also help fight widespread counterfeit drugs in developing countries. For example, two startups Veripad and slalom have created a system which allows everyone to test drugs . The first component of this system is the so-called Paper Analytical Device (PAD) – a chemical test card that can identify components of common medications in minutes without any lab equipment. You just crush one tablet of the medication you want to test, rub it over the card, and soak the card in water. As a result, the PAD creates a color barcode that describes which components are included in the tested medicine. However, to interpret this barcode, one needs specific knowledge. To make these tests accessible to everyone, the startups have created an app that takes a picture of the barcode and utilizes an ML-algorithm to interpret the test result. With this approach anyone can test suspicious drugs without any special education. So, people will not suffer the harms of fraudulent or substandard medication.
Machine Intelligence technologies are already significantly improving healthcare in various ways. However, there must be much more effort to enable developing countries to participate in this development. Thus, think creatively and use the power of modern technologies to improve people’s health all over the world !
 We, at MI4People, are eager to collaborate with you to help you understand the potential of MI and identify impactful research projects that we can undertake to find MI based solutions that help deliver Public Good better as well as assist you in meeting your organization's goals more effectively.