By 2030, the global general computing power (FP32) will reach 3.3 ZFLOPS, a 10-fold increase over 2020
Smart Health Services Enhance Quality of Life
By 2030, ICT technology will further shift the healthcare system from traditional diagnosis and treatment to full-lifecycle health management. With ongoing AI innovations, accessible healthcare services will significantly enhance quality of life.
Predictions
By 2030, the global general computing power (FP32) will reach 3.3 ZFLOPS, a 10-fold increase over 2020
There will be 864 ZFLOPS of AI computing power (FP16), a 4,000-fold increase over 2020
Directions for Exploration
Unlocking the value of health data, and shifting the focus from reactive treatment to proactive prevention
According to the WHO, 60% of related factors to illnesses are correlated to lifestyle, making healthy habits essential for well-being. With user consent, wearable devices can collect and analyze real-time health data, and offer predictive insights and medical guidance with the assistance of a unified AI architecture. This shift toward proactive prevention integrates health management into daily life, connecting disease control, hospitals, health centers, and families to reduce the risk of a more serious health condition arising.
Snapshot from the future: Focusing on real-time health status monitoring to facilitate real-time, efficient health management
Thanks to the advancements in the Internet, IoT, and AI, as well as the widespread adoption of wearable devices and home monitoring equipment, by 2040, at least a quarter of outpatient care, preventive care, long-term care, and health services will move online.
Specifically, these technologies will be used to analyze real-time health data, medical responses, and clinical outcomes to identify potential health risks. For example, AI can detect the early signs of heart disease or pre-diabetic conditions by analyzing and warning users about anomalous heart rate and blood pressure measurements, allowing for early intervention. Additionally, through interventions in nutrition, exercise, and sleep, users can be guided to gradually change their unhealthy habits and develop a healthier lifestyle, reducing the likelihood of illness. For instance, a study by Stanford University showed that continuous monitoring of heart rate and skin temperature through smartwatches and other wearables could help AI detect early signs of infection, as these powerful monitoring devices can take and analyze up to 250,000 measurements per day.
Moreover, a comprehensive health management platform could allow hospitals, doctors, users, and families to collectively access and view health data. This data-sharing mechanism ensures that doctors can stay updated on their patients' health in real time, both inside and outside the hospital, leading to more accurate diagnoses and better treatment decisions.
Snapshot from the future: Using intelligent disease prediction and prevention to enhance public health response capabilities
By integrating electronic health records (EHRs), wearable device data, lab results, and public health data from a variety of sources, AI systems can build comprehensive health databases. Using machine learning and deep learning, AI will analyze this data to identify patterns related to the incidence of disease and develop predictive models. These models will provide early warnings about the potential risk of being affected by certain diseases at both the individual and population levels, allowing for timely preventive action.
For example, researchers are using AI and big data to analyze global public health data and epidemiological information and to develop models capable of predicting outbreaks of infectious diseases such as influenza. These models can identify potential hotspots and transmission pathways, allowing health organizations to implement preventative measures before outbreaks spread.
The intelligent disease prediction and prevention system enables public health organizations to swiftly respond to epidemic threats and adopt effective control measures, significantly reducing the societal impact of outbreaks. It not only enhances public health response capabilities but also improves individual health management, and this contributes to the improvement of overall public health.
Enabling healthcare with digital intelligence to improve quality and accessibility
The integration of AI into healthcare not only safeguards public health but also drives improvements in economic and social development. By using advanced technologies, digital healthcare significantly enhances the quality and accessibility of medical services. AI and machine learning algorithms can analyze vast amounts of medical data and assist doctors in making precise diagnoses and developing personalized treatment plans. This improves treatment outcomes, enhances patient satisfaction, and reduces the likelihood of a misdiagnosis.
Digital healthcare systems also optimize resource allocation by ensuring balanced coverage across regions, including in remote and underserved areas. Through intelligent scheduling and resource management, these systems ensure that healthcare services are available to more patients, and this improves access to timely and high-quality medical care.
Snapshot from the future: Smart healthcare innovations for enhanced diagnostic efficiency and precision
In the future, digital and AI-powered medical care will lead to significant improvements in efficiency and precision. AI and big data will play a crucial role in medical imaging in particular. Using deep learning algorithms, AI can analyze large datasets of medical images, such as X-rays, CT scans, and magnetic resonance imaging (MRI) scans, and detect lesions and provide accurate diagnostic recommendations. This will not only enhance screening efficiency but also reduce the risk of misdiagnosis. For example, research shows that AI outperforms traditional methods in early breast cancer detection.
Furthermore, AI will be integrated with electronic medical records (EMRs) to continuously update patient health data in real-time. This real-time data will help AI develop personalized treatment plans, predict disease risks, and assist doctors in making more effective treatment decisions, ultimately improving patient outcomes.
AI will also play a significant role in pathology screening, where it can be used to analyze pathology slides and detect abnormal cell and tissue changes. This assists pathologists in making faster and more accurate diagnoses. AI's ability to detect cancer cells in pathology images has already been widely recognized.
Big data and AI will also play an important role in helping healthcare organizations and insurance companies manage expenses more intelligently and thus help to keep healthcare costs reasonable for patients. By analyzing vast amounts of medical data, AI can predict healthcare cost trends, identify unnecessary expenses, and detect potential fraud. This smart cost-control method not only protects patients' interests but also optimizes the use of healthcare resources.
Snapshot from the future: Enabling AI-driven, all-domain, collaborative healthcare to optimize resource allocation
The future of healthcare will be revolutionized by modern communications and information technologies. They will extend medical services to remote monitoring, consultation, and treatment. AI and foundation models will be central to this revolution. Clinical decision support systems (CDSs) are particularly important. By leveraging deep learning and machine learning, CDSs analyze medical images and EHRs to provide accurate and efficient diagnoses and personalized treatment solutions. These systems can quickly supply essential diagnostic information to doctors in remote areas, leading to more accurate decisions and treatments. For example, in Türkiye there were plans to develop a web- and mobile-based application which would allow doctors to remotely monitor patient data in real time and which would unify and enable the screening, diagnosis, treatment, and monitoring of diabetes diseases.
Personalized health management is another key area. By analyzing both historical and real-time health data, AI can predict health risks and offer tailored recommendations to help individuals adjust their lifestyles and prevent diseases. These technologies show a lot of promise in chronic disease management; for example, remote monitoring of hypertension allows for timely interventions and significantly improves health outcomes. In all-domain healthcare collaboration, continuous data monitoring and analysis enable remote doctors to provide more precise health guidance.
Large language models (LLMs) help patients by answering questions in real time, thereby easing the burden on remote doctors. These models provide medical advice, psychological support, and can also assist in making appointments with doctors, making healthcare more efficient. For instance, virtual assistants can analyze conversations with patients to offer personalized suggestions which can help patients improve their mental health.
Video communication technologies also play a vital role, as they allow doctors to conduct remote consultations and diagnostics, increasing the convenience and accessibility of medical services. For example, the Remote Hypertension Improvement Program uses video calls and remote monitoring to efficiently lower patients' blood pressure.
Overall, AI and foundation models elevate the quality and efficiency of healthcare services, enabling personalized health management and optimized resource allocation, and driving the development of more inclusive healthcare services.
Multidisciplinary integration and its role in driving innovation in medical research
The integration of multiple disciplines is a key frontier to drive medical research innovation today. Advancements in biomedical engineering, information technology, AI, and big data, are driving a significant transformation in medical research. For instance, nanotechnology in drug delivery systems enhances treatment precision and reduces side effects. Big data analysis allows researchers to uncover new disease patterns and develop novel treatment methods which advance personalized medicine. Meanwhile, AI can be used in combination with medical images and EHRs to accelerate and improve the accuracy of diagnostics. This multidisciplinary approach accelerates the research-to-clinical-practice process, drives rapid advancements in medical technology, and establishes a solid foundation for the future of healthcare.
Snapshot from the future: Multimodal data integration for more advanced precision medicine
Multimodal data integration is set to revolutionize precision medicine. By combining genomics, proteomics, metabolomics, imaging data, and clinical information, researchers can gain a more comprehensive understanding of disease mechanisms. For example, AI can predict disease risks more accurately and develop personalized treatment plans with multimodal data. A study in Nature Medicine has shown how the convergence of generative AI and LLMs in medical imaging has opened up new ways to harness the power of both visual and textual information. Integrating these advanced technologies enables multimodal data integration, representation learning, and improved clinical decision support systems. Multimodal models built upon generative AI and LLMs can integrate the visual features of medical images with contextual information from radiology reports or EHRs to facilitate various medical-image processing tasks. LLMs can process radiology reports to extract pertinent information, match them with the corresponding images, and generate natural-language summaries that can enhance communication between healthcare professionals and facilitate better decision-making when it comes to patient care. By leveraging patient-specific information, such as genetic data, medical history, and lifestyle factors, and evaluating it in conjunction with medical images, these AI models can facilitate more efficient treatment and diagnoses for patients.
Snapshot from the future: The rise of intelligent medicine and its role in driving industry transformation
The future of medicine is poised to move from a one-size-fits-all approach to a more bespoke and patient-centric approach. Key factors such as the physical condition of the patient, appropriate drug types, timing, dosage, and the treatment duration must be taken into consideration when designing drug treatment plans. These plans, once formulated, also need to be regularly updated based on the treatment effect and progress. This puts significant pressure on doctors, and they are often forced to rely on general expertise and experience rather than the patient's specific symptoms and indicators to quickly formulate a general treatment plan. However, with AI and foundation model technologies, vast amounts of pathological data can be analyzed in real time, enabling doctors to offer more personalized treatment recommendations. For example, a research institute in Singapore has developed an AI-powered platform that evaluates medication effectiveness. The platform can quickly analyze a patient's clinical data, provide the patient with a personalized prescription, and modulate tumor sizes or biomarker levels in the patient's profile based on available data.
In drug development, AI has moved beyond the proof-of-concept stage. Advanced machine learning technologies are accelerating the pace of innovation, reducing evaluation times, and enabling the exploration of new areas of medicine. In practice, AI R&D tools improve the speed of ingesting, structuring, and extracting inferences from scientific literature by a factor of 1,000. AI-driven simulations run 2 to 40 times faster, and AI models can propose new hypotheses 10 times faster than before. Autonomous AI-powered laboratories can conduct experiments 100 times faster than before. This reduction in manual data processing and information handling has increased the overall speed of drug discovery tenfold.
For example, Insilico Medicine, an AI pharmaceutical company, launched the world's first automated AI-assisted decision-making laboratory. The lab integrates AI with automation, robotics, and biological capabilities, and can complete the entire cycle of target discovery and validation within 14 days.
Looking ahead, AI will drive cost reduction and efficiency gains in the pharmaceutical industry, accelerate drug development, and bring unprecedented changes to this industry.
More Outlooks
