Introduction
Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, and decision-making. In recent years, AI has seen rapid advancements and innovations led by growth in computational processing abilities, the availability of vast data, and progress in machine learning algorithms.
Healthcare is one of the sectors witnessing extensive AI adoption today. From diagnosis to treatment and drug discovery, AI is demonstrating enormous potential for solving some of the most pressing challenges faced by global healthcare systems. Let's analyze the implementations and future possibilities in detail.
Current Healthcare Challenges Facing Global Health Systems
Before studying AI use cases, it helps to examine why healthcare needs an urgent transformation worldwide in the first place:
1. Demographic Shifts - Populations are aging and chronic diseases rising rapidly. The WHO estimates that people over 60 will outnumber under 5s by 2030. Caring for large aging groups with chronic conditions puts healthcare systems under extreme stress.
2. Doctor Shortages - The aging population also means more demand for doctors. However with physician burnouts and early retirements, doctor shortfalls are becoming severe across regions including India, Singapore, Australia, Canada, posing threats to patient care quality and access.
3. Increasing Costs - Global healthcare spends are ballooning, expected to touch $10 trillion by 2022. US costs may reach $6 trillion in a decade, a third higher than recent figures. Most citizens struggle to afford quality care, while insurers and governments face financial pressures.
It is in this context that AI promises to provide innovative solutions – from making healthcare affordable, accessible and efficient to saving lives through early interventions. Let us analyze key focus areas:
A. Improving Diagnostics
1. Medical Imaging and Diagnosis
Radiology is a prime area where AI imaging analysis helps doctors screen disease manifestations from MRI, X-ray, CT scan visuals faster and with higher accuracy. Systems can detect small variations invisible to humans andsuggest insights per statistical disease models. For instance Enlitic's product screened lung CTs 50% more accurately than radiologists in clinical trials. Israel's Zebra Medical Detects strokes, heart disease, osteoporosis etc. from body scans at 90% accuracy. Such tools significantly boost diagnostics productivity. Companies like Qure.ai and Infervision from China also offer AI imaging platforms for markets like India addressing radiologist gaps.
2. Early Warning Systems
AI has produced promising results in designing algorithms that serve as early warning systems by tracking indicators over time. Researchers created models that predict risk of sepsis, a life threatening infection response, 24 hours prior just by assessing vital signs, saving critical delays. Similar monitoring has shown up to 80% accuracy at forecasting heart failure possibilities via biomarkers tracking allowing preventative care. If incorporated widely at critical care centers and surgical wards, such predictive AI could provide life-saving alerts and drastically cut-down mortality rates.
3. Pathology and Genomics
AI imaging analytics is accelerating cancer pathology by automating aspects like labeling tumor regions accurately for biopsy focus, detecting cancer strains through microscopic specimen review and grading tissue anomalies. Israeli startups Ibex Medical Analytics, Nucleai exemplify such efforts. In genomics, AI systems like BERG Analytics allow doctors to model patients' molecular profiles against wider disease databases to predict effectiveness of gene-specific therapies like immunotherapy in combating aggressive cancers. This enables accurate drug recommendations.
B) Assisting Health Monitoring and Patient Care
1. Virtual Nursing Assistants
AI-powered chatbots are playing the role of virtual nurse assistants by tracking patient health data, answering common questions about medications or measures, reminding about follow-ups and encouraging care plan adherence. Early implementations have reduced the burden on actual nursing staff in hospitals while ensuring improved outcomes through continuous engagements. Patients find the tools helpful in managing conditions without in-person visits. Moreover, the AI keeps learning from conversations, enhancing capabilities over time.
2. Chronic Condition Management
Wearables powered by machine learning track hundreds of biometrics continuously, analyzing deviations to send alerts on any likelihood of complications in chronic heart or diabetes cases. This allows early interventions and superior management than sparse in-clinic appointments. For instance recently approved in the US, the KardiaMobile device by AliveCor monitors heart rhythms using AI to detect likelihood of strokes through ECG analysis on mobiles within 30 seconds. Such smart remote care promises better outcomes.
C) Revolutionizing Medical Support Services
1) Automating Admin workflows
Healthcare involves innumerable administrative processes from admissions, accounting, compliance to procurement. These are tedious tasks reducing doctor bandwidth for actual duty. AI powered Buoy Health demonstrates the change possible. Their tool handles end-to-end patient appointment bookings by digitizing scheduling coordination, documentation and even billing - saving providers over 20 hours per doctor monthly. Similar productivity benefits are achievable across admin verticals including inventory and pharma ordering if systems turn cognitive.
2) Chatbot and Robotic Process Automation
Many hospitals and insurance firms now use chatbots or software robots to resolve repetitive customer queries, update records, or even process claims and bills thereby reducing manual overheads. These encode query patterns and integration capabilities to either respond independently or trigger right workflows accurately. While enhancing productivity and accuracy, such assistants ensure consumers get quick resolutions rather than awaiting available agents. Progressively advanced NLP integration is improving how intelligently these bots can converse, tapping AI's potential in revolutionizing support functions through automation.
3) Precision Medicine and Drug Discovery
The genomics revolution has opened amazing possibilities for truly personalized therapies tailored to individual DNA profiles, allergies and biomarkers rather than the one-size-fits-all status quo. AI assists doctors in modeling probable outcomes of different drug options customized for a patient's vast molecular data, recommending the optimal match for tackling the person's version of a disease based on results manifesting across wider population groups historically. This extends life expectancies.
Moreover, AI systems like Atomwise, Exscientia, Insilico Medicine discover promising new molecular molecules with desired properties faster by virtually screening billions of combinations using algorithms. This accelerates the most resource intensive step of drug creation manifold while boosting success probabilities and is invaluable for humanity.
Conclusion
In essence, artificial intelligence innovations are introducing a paradigm shift in global healthcare systems today by assisting providers, connecting caregivers and patients better and fueling crucial discoveries at unprecedented scales.
Adoption is still in early phases and faces barriers like change inertia and data privacy concerns that need smart policy-making. But AI's healthcare contributions have encouraged significant investments and pilots across diagnosis, care coordination, patient engagement and medical insights areas that will shape a radically different landscape in the coming decade.