Can Myopia Be Reduced from 800 to 100 Degrees? What Are the AI Health Applications?
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Recently, short videos claiming 'myopia reduction from 800 to 100 degrees' went viral, sparking widespread attention. Various hosts demonstrated so-called 'recovery' methods in these videos. However, investigations reveal that most of these videos are pseudoscientific, with questionable scientific validity and effectiveness. In the fields of ophthalmology and vision recovery, there is no evidence that such significant myopia improvement can be achieved through simple methods or non-medical interventions. Therefore, these videos are likely misleading, especially for those seeking myopia treatment.
For myopia treatment, the most important step is to consult professional ophthalmologists and follow their advice. Although modern medicine and technology have made significant progress in vision correction—such as laser surgery and orthokeratology—these methods must be performed under professional medical supervision. Additionally, maintaining healthy lifestyle habits, including proper eye rest, a balanced diet, and regular physical activity, is crucial for vision care.
What Are the AI Health Applications?
1. AI-Assisted Medical Imaging Analysis
Artificial intelligence serves as a tool for case analysis. It supports clinicians in reviewing images and scans, enabling radiologists or cardiologists to identify critical insights for important cases, avoid potential errors when reading electronic health records, and establish more accurate diagnoses. Clinical research can generate vast amounts of data and images. AI algorithms can quickly analyze these datasets and compare them with other studies. This process enables medical imaging professionals to rapidly track critical information.
Detecting relevant issues and presenting them to radiologists in a summarized view allows for the design of more customized, targeted, and accurate reports for diagnostic decision-making.
2. AI Can Reduce Drug Development Costs
Supercomputers have been used to predict which potential drugs from molecular structure databases may be effective against various diseases. By using convolutional neural networks (a technology similar to that enabling self-driving cars), it's possible to predict how small molecules bind to proteins by analyzing hints from millions of experimental measurements and thousands of protein structures.
This process enables convolutional neural networks to identify safe and effective drug candidates from search databases, thereby reducing drug development costs.
3. AI Analyzes Unstructured Data
Due to the massive volume of health data and medical records, clinicians often struggle to stay updated with the latest medical advancements while providing patient-centered quality care. EHR and biomedical data compiled by healthcare institutions and professionals can be rapidly scanned using ML technologies, providing clinicians with timely and reliable answers. In many cases, patients' health data and medical records are stored as complex unstructured data, making interpretation and access difficult. AI can search, collect, store, and standardize medical data in any format, assist with repetitive tasks, and support clinicians in providing patients with fast, accurate, and tailored treatment plans and medications - rather than being buried under the heavy workload of searching, identifying, collecting, and transcribing solutions they need from piles of paper EHRs.
4. AI Can Predict Kidney Disease
Clinicians often struggle to detect Acute Kidney Injury (AKI), which can cause rapid deterioration and become life-threatening for patients. Failure to identify and treat these cases accounts for an estimated 11% of hospital deaths. Early prediction and treatment could significantly impact the reduction of lifelong treatment and kidney dialysis costs.
5. AI Provides Valuable Assistance to Emergency Medical Personnel
During sudden cardiac arrest, the time between calling 120 and ambulance arrival is crucial for recovery. To increase survival chances, emergency dispatchers must recognize cardiac arrest symptoms to take appropriate action. AI can analyze verbal and non-verbal cues to establish diagnoses remotely.
6. AI Accelerates the Discovery and Development of Genetic Medicine Artificial intelligence is also used to help rapidly discover and develop drugs with high success rates. Genetic diseases are favored by altered molecular phenotypes, such as protein binding. Predicting these changes means predicting the likelihood of genetic diseases occurring. This can be achieved by collecting data on all identified compounds and biomarkers associated with certain clinical trials.
- AI Supports Health Equity
The AI and ML industry has a responsibility to design smart healthcare systems and tools to ensure fairness and equality in data science and clinical research, thereby delivering optimal health outcomes. With the increasing use of machine learning algorithms across various medical fields, there is a risk of health inequities emerging.
Those responsible for applying AI to smart healthcare must ensure that AI algorithms are not only accurate but also objective and fair. Since many clinical trial guidelines and diagnostic tests consider patients' race and ethnicity, debates have arisen: Are these factor selections evidence-based? Is race and ethnicity data more likely to address or exacerbate widespread health inequities? It has been established that ML comprises a set of methods that enable computers to learn from the data they process. This means, at least in principle, ML can provide unbiased predictions based solely on impartial analysis of underlying data. Artificial intelligence and machine learning algorithms can reduce or eliminate bias by improving data transparency and diversity, thereby reducing health inequalities. AI and ML's smart healthcare research has the potential to eliminate disparities in health outcomes based on race, ethnicity, or gender.