Why using AI in healthcare requires a balance of efficiency and ethics
While healthcare organizations are increasingly adopting machine learning, questions will grow about the extent to which it will dictate decision making.
Along with predicting epidemics, diagnosing diseases and counseling patients, artificial intelligence is also proving its worth in healthcare delivery to enable a better patient experience.
From making sense of the unwieldy mass of medical data trapped in healthcare systems to tapping into the collective knowledge gathered from several thousand healthcare providers and millions of patient visits, doctors can now start to analyze which treatments work best and when.
Now, AI can recommend a line of action even in clinically challenging situations, assisting radiologists in analyzing simple cases, prescribing a first line of treatment to patients before they see a doctor and help monitor health and medication in chronic conditions.
Changes in healthcare delivery and its ever-evolving expectations are forcing greater adoption of technology to manage patient information and assist in the decision-making process. The need for data mining and improved analytics and decision-making has put AI at the heart of this transformation. Healthcare as an industry is a leading beneficiary of the greater accessibility, relevance and actionability of information that AI enables.
A recent global study of 1,600 IT and business decision makers from healthcare and industries confirms this view of AI’s transformational power. The study found 58 percent of pharmaceuticals and life sciences companies have some level of AI adoption, leading nine other industry sectors including automotive and aerospace, manufacturing, and retail. In addition, 50 percent of healthcare organizations surveyed said they have implemented AI.
The healthcare sector faces particularly tough challenges with technology advances, including concerns around privacy, data silos and ethics, yet its AI adoption level is ahead of industries such as retail and financial services, according to the survey data.
The deployment of AI is projected to grow dramatically. The prognosis is that pharmaceutical companies, medical devices companies, providers, caregivers and technology companies will eventually come together to improve overall healthcare beyond selling their individual offerings. When this happens, AI will be a key driver of “connected healthcare,” addressing patient needs end-to-end, and even helping to prevent people from falling ill.
According to the Infosys study, titled Amplifying Human Potential: Towards Purposeful Artificial Intelligence, 52 percent of pharmaceutical respondents are building AI into the fabric of their companies and are beginning to see results. New innovations are changing the face of drug discovery by leveraging AI and machine learning for search and analysis, research, simulation studies and even hyper-targeting to potentially tighten the long lead time for launching a new drug in the market.
However, the ethics of allowing AI and associated machines into decision making in the healthcare field could be a barrier to future adoption.
Algorithms will become smarter and more accurate, yet there is always potential for a data or processing error that may result in a misdiagnosis, incorrect radiology reading or other medical course of action that a professional would not support.
These scenarios are a supporting argument for the theory that AI and automation in healthcare will not supplant or replace human workers, but serve to amplify their abilities. While AI and machine learning relies on millions of analyses of data alone for diagnosis, a healthcare practitioner, in addition to diagnostic data, also uses intuition and experience in making a medical decision.
Among pharmaceuticals and life sciences professionals, 53 percent responded that their organization has fully considered the ethical implications of AI. There are many ethical issues that can be confronted by carefully setting boundaries for tasks that AI can and cannot perform.
The key principle is to leverage the full power of AI in clinical settings to assist, enable and co-work with healthcare professionals, who continue to remain in charge of decisions of critical importance, such as diagnosis and treatment.
It will benefit society to maintain an open mind about how decision-makers in healthcare organizations can work alongside AI and selectively rely on it to inform and improve care. As an early leader in adopting the technology, it may help dispel the prejudices and myths surrounding AI, and build basic awareness and education among working professionals in the medical field and beyond.
The industry also needs to establish ethical standards and obligations for the organization as well as metrics to assess the performance of AI systems. Workers in healthcare that are displaced from their current roles by automation are being retrained and reskilled to perform new ones, and it’s possible that redirecting a significant portion of that talent to operate and manage ethics with AI will prove worthwhile.
From making sense of the unwieldy mass of medical data trapped in healthcare systems to tapping into the collective knowledge gathered from several thousand healthcare providers and millions of patient visits, doctors can now start to analyze which treatments work best and when.
Now, AI can recommend a line of action even in clinically challenging situations, assisting radiologists in analyzing simple cases, prescribing a first line of treatment to patients before they see a doctor and help monitor health and medication in chronic conditions.
Changes in healthcare delivery and its ever-evolving expectations are forcing greater adoption of technology to manage patient information and assist in the decision-making process. The need for data mining and improved analytics and decision-making has put AI at the heart of this transformation. Healthcare as an industry is a leading beneficiary of the greater accessibility, relevance and actionability of information that AI enables.
A recent global study of 1,600 IT and business decision makers from healthcare and industries confirms this view of AI’s transformational power. The study found 58 percent of pharmaceuticals and life sciences companies have some level of AI adoption, leading nine other industry sectors including automotive and aerospace, manufacturing, and retail. In addition, 50 percent of healthcare organizations surveyed said they have implemented AI. The healthcare sector faces particularly tough challenges with technology advances, including concerns around privacy, data silos and ethics, yet its AI adoption level is ahead of industries such as retail and financial services, according to the survey data.
The deployment of AI is projected to grow dramatically. The prognosis is that pharmaceutical companies, medical devices companies, providers, caregivers and technology companies will eventually come together to improve overall healthcare beyond selling their individual offerings. When this happens, AI will be a key driver of “connected healthcare,” addressing patient needs end-to-end, and even helping to prevent people from falling ill.
According to the Infosys study, titled Amplifying Human Potential: Towards Purposeful Artificial Intelligence, 52 percent of pharmaceutical respondents are building AI into the fabric of their companies and are beginning to see results. New innovations are changing the face of drug discovery by leveraging AI and machine learning for search and analysis, research, simulation studies and even hyper-targeting to potentially tighten the long lead time for launching a new drug in the market.
However, the ethics of allowing AI and associated machines into decision making in the healthcare field could be a barrier to future adoption.
Algorithms will become smarter and more accurate, yet there is always potential for a data or processing error that may result in a misdiagnosis, incorrect radiology reading or other medical course of action that a professional would not support.
These scenarios are a supporting argument for the theory that AI and automation in healthcare will not supplant or replace human workers, but serve to amplify their abilities. While AI and machine learning relies on millions of analyses of data alone for diagnosis, a healthcare practitioner, in addition to diagnostic data, also uses intuition and experience in making a medical decision.
Among pharmaceuticals and life sciences professionals, 53 percent responded that their organization has fully considered the ethical implications of AI. There are many ethical issues that can be confronted by carefully setting boundaries for tasks that AI can and cannot perform.
The key principle is to leverage the full power of AI in clinical settings to assist, enable and co-work with healthcare professionals, who continue to remain in charge of decisions of critical importance, such as diagnosis and treatment.
It will benefit society to maintain an open mind about how decision-makers in healthcare organizations can work alongside AI and selectively rely on it to inform and improve care. As an early leader in adopting the technology, it may help dispel the prejudices and myths surrounding AI, and build basic awareness and education among working professionals in the medical field and beyond.
The industry also needs to establish ethical standards and obligations for the organization as well as metrics to assess the performance of AI systems. Workers in healthcare that are displaced from their current roles by automation are being retrained and reskilled to perform new ones, and it’s possible that redirecting a significant portion of that talent to operate and manage ethics with AI will prove worthwhile.
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