We believe that improving access to healthcare will help to end extreme poverty and boost shared prosperity. 

Technology has always played an important role in the delivery of healthcare. Artificial intelligence (AI) has been used to help doctors identify cancers in medical images since the late 1990s and has been applied broadly in the response to COVID, including in emerging market economies. 

AI is a broad term for computer systems that can continuously scan their environment, learn from it, and take action in response to what they are sensing and human-defined objectives. 

AI techniques have seen rapid progress over the past decade. However, AI will only be effective if trust is built between data providers, health-tech companies, regulators, governments, and the public.

Investment in health businesses using AI is growing as the number of health-tech deals and funding has increased rapidly over the past decade, and AI-specific investments are accounting for an increasing portion of total health-tech funding. 

The intended application of these investments ranges from digital diagnosis to clinical decision support and precision medicine.

The integration of AI into health businesses will help deliver and scale development impact.

Health-tech businesses are finding applications for AI across the health ecosystem, from drug discovery, imaging and diagnostics technology, and genomics, to delivering health system efficiencies and enhanced customer relationship management. 

Non-traditional players such as digital-tech giants are leveraging their extensive databases to compete with traditional providers. Current investment activity in AI for healthcare is focused on operational efficiencies in developed markets, but there are risks and challenges to scaling these applications and business models across emerging market economies.

In emerging market economies, Artificial Intelligence in healthcare is not just technology, but a transformative force that holds the potential to reshape the future of healthcare delivery. 

By augmenting limited resources, improving diagnostics, enabling personalized treatments, and fostering data-driven decision-making, AI empowers these economies to bridge the healthcare gap, enhance patient outcomes, and ultimately build healthier and more prosperous societies.   

Health data is amassed from a range of sources, including electronic medical records, payer records, wearables and mobile phones, genomic sequencing, medical research, and mandated government records. AI can be used to analyze this data to facilitate better care.

Companies are helping to tackle the weakness in data interoperability by cleaning and structuring data and overlaying analytics to make meaningful predictions to improve health. These companies are applying machine learning and human language processing to structure millions of clinical EMRs into research-grade data.

Data will be used to move the dial on the global health system from treatment to prevention of non-communicable diseases. AI can be applied to big data to provide personalized and responsive ancillary health services that 'nudge' consumers toward preventative behaviors. 

For example, health insurance companies may use a 'shared value' business model, which uses machine learning to determine what financial rewards each customer receives for positive health behaviors.

Software-as-a-service providers are leveraging a wide variety of data sources to automate systems, driving more effective services and addressing system inefficiencies.

Machine learning can be used to personalize patient care and use behavioral analytics to segment patients by behavioral archetypes and then personalize treatment strategies for each patient.

Consumers in emerging market economies are more open to digital healthcare, and this opens up a huge market for solutions that reduce the cost of reaching patient segments that were too costly using traditional business models.

Health-tech companies can leverage AI to provide 24/7 remote appointments and treatment advice, and with the help of chatbots interpret users' symptoms and combine this information with public information and the patient's medical history to provide relevant health and triage information, including whether further care should be sought.

AI-enabled diagnostics can help specialists, especially in areas with a shortage of health infrastructure and specialized professionals, such as pathologists, radiologists, and cardiologists, by detecting critical findings in CT exams and highlighting the findings for radiologists to help them prioritize urgent patients.

Technologies like AI are facilitating the development of precision medicine, a data-driven approach to medicine that accounts for variability in genes, environment, and lifestyle factors to personalize medical care. The cost of whole human genome sequencing has fallen from $4,000 to $1,500 between mid and late 2015 alone.

AI-enabled health solutions are increasingly being developed for use cases in emerging markets, particularly in response to the COVID pandemic.

However, technological barriers such as access to and affordability of smart devices, digital connectivity gap, and digital literacy remain important impediments.

Healthcare is a high-stakes game, so regulatory frameworks are rigorous.

AI innovations are fed by data, and the commercialization of technologies must navigate a plethora of data regulations. There is a trade-off between high standards for patient consent, data privacy, and data protection, and the need for large structured datasets.

Given the sensitivity of health data, it is difficult to construct big data sets to apply AI technologies.

Also, the use of machine learning in clinical diagnostic applications carries several inherent risks. For example, a system may make a misdiagnosis due to a mismatch between the data it is trained on and the data it is used in operation, to black-box decision making, to insensitivity to impact, or to negative unintended consequences.

Given the risks, there are legitimate questions about when AI technologies are appropriate for use in patient diagnosis and treatment. 

Robust clinical trials are needed to determine whether algorithms make better decisions than clinicians in some cases.

Many health-tech innovations are a result of rigorous academic research. However, the most successful health start-up ecosystems are concentrated in the United States, and countries with the greatest health gaps have the largest hurdles to developing and diffusing locally appropriate solutions for some healthcare needs. 

Businesses can help accelerate trust in AI by having clear terms of use and consent, being transparent in the intended use of data, and investing in human capital.

TO SUM UP:

Technology has always played an important role in the delivery of healthcare, and artificial intelligence (AI) has been used to help doctors identify cancers in medical images since the late 1990s. 

The current pandemic has highlighted the potential beneficial application of AI technologies in healthcare by improving diagnostics, improving service affordability, guiding consumers in preventative behaviors, and improving transparency in the quality of services.

Investment in health businesses using AI is growing, and AI-specific investments are accounting for an increasing portion of total health-tech funding. AI is finding applications across the health ecosystem, from drug discovery to clinical decision support and precision medicine. 

AI can be used to analyze health data from a range of sources, including electronic medical records, payer records, wearables and mobile phones, genomic sequencing, medical research, and mandated government records.

Data will be used to move the global health system from treatment to prevention of non-communicable diseases, and AI will be used to 'nudge' consumers toward preventative behaviors. 

AI-enabled diagnostics can help specialists in areas with a shortage of health infrastructure and specialized professionals prioritize urgent patients by detecting critical findings in CT exams. 

Technologies like AI are facilitating the development of precision medicine, which accounts for variability in genes, environment, and lifestyle factors.

AI-enabled health solutions are increasingly being developed for use cases in emerging markets, but technological barriers remain important impediments. The use of machine learning in clinical diagnostic applications may carry several inherent risks, including misdiagnosis due to a mismatch between the data it is trained on and the data it is used in operation. 

Institutional inertia is preventing many health-tech innovations from spreading globally. Businesses can help accelerate trust in AI by being transparent and investing in human capital.

The game-changing potential of AI technologies in improving the speed, affordability, remote access, and preventative focus of health-tech innovations requires an ecosystem in which investors, regulators, technologists, medical and research professionals, and consumer advocates develop consensus on regulatory frameworks and ethical boundaries.