Fusing AI and Healthcare
A Prescription for Success.
The transformational impact of Artificial Intelligence (AI) is evident across various industries, but within the realm of healthcare, this impact could be life-altering. From clinical research and drug development to hospital care and insurance, AI's footprint in the health sector could revolutionize operations, cut costs, and improve patient outcomes.
AI investment in healthcare is expected to reach a remarkable $6.6 billion by 2021, combining public and private sector investments. Accenture predicts that top-level AI applications may lead to an annual savings of $150 billion by 2026.
The benefits of this transformative technology will be accumulated gradually. Automated operations, predictive diagnostics facilitating preventive interventions, and precision surgery are prominent examples. Within the next decade, these benefits will fundamentally reshape the healthcare sector.
Additionally, AI can potentially solve the intricacies of the "iron triangle" in healthcare. In this concept, three interrelated factors — access, affordability, and effectiveness — often require unavoidable and mostly negative trade-offs. Introducing AI into this scenario could improve one element without harming others, thus breaking the traditional constraints.
The solution to the persistent cost-structure problem in the current healthcare system lies in shifting time-consuming tasks, traditionally carried out by humans, to machines. This will enable patients to address their care needs independently whenever possible and significantly reduce the required human labor to maintain a healthier population.
Experts from Accenture believe that AI could address around 20% of unmet clinical demand presently. Healthcare providers, ranging from drug manufacturers to major hospitals, are taking note, with over one-third of provider executives revealing that they were investing in AI, machine learning, and predictive analytics as of 2018.
Despite all the potential benefits of AI, some significant hurdles must be overcome for its widespread adoption and implementation. Healthcare providers must trust AI algorithms before using them, which will require substantial clinical validation. Also, patients are skeptical and reluctant about AI-powered health services, primarily due to a need for more understanding of the technology.
The scale of AI adoption is another critical issue. Pilot programs with a limited scope may not readily adapt to large-scale institutions. Advanced AI solutions might also be too costly initially for small regional and rural healthcare providers.
Considering the vast scale of the health industry and the diverse stakeholders involved, there is expected to be resistance and pushback as AI transforms healthcare treatment and operations. However, with the amount of investment and the potential savings, alongside the improvement in patient care, the future of AI in healthcare is promising.