AI Medicine is a field that uses machine learning and artificial intelligence techniques to analyse medical data and make recommendations or predictions about patient care or drug development. These systems can be used to support a variety of medical tasks, including diagnosis, prognosis, treatment planning, and monitoring of patient health.
For example, predictive analytics systems can be employed to identify patients who are at high risk of developing a particular condition or disease, based on their medical history, demographics, and other factors. Complementary to this, diagnostic systems that use AI to analyse medical images or other diagnostic data to identify abnormalities or disease have been maturing regarding early cancer detection employing, both bio-marker-based and visual-based-data.
Also, AI-based drug discovery incorporating large amounts of data, such as genomic data, chemical compounds, and clinical trial results, can be used in order to identify potential drug candidates and predict their effectiveness.
This can speed up the drug discovery and development process that currently takes about 10 years by automating tasks, such as data analysis and molecule design, and by identifying patterns and relationships that may not be immediately apparent to humans. AI can also help to reduce the cost of drug development that averages out to about US$ 2.6bn for bringing a new drug to the market by identifying promising drug candidates more quickly and efficiently.
- Improving diagnostic accuracy and reducing the risk of missed or incorrect diagnoses
- Patient data can be analysed to recommend the most appropriate treatment options based on the patient’s individual characteristics and circumstances
- The potential to significantly improve the speed, efficiency, and effectiveness of the drug development process can support the development of more targeted and effective treatments for a wide range of diseases
- By automating certain tasks, such as data analysis and decision-making, AI systems can help to reduce the workload of healthcare providers and free up their time to focus on other important tasks
- In case the data used to train the AI system is biased, it may produce biased or unfair results, also the system may be less accurate at diagnosing individuals from other patient groups
- Vulnerability to cyber attacks could compromise patient data and disrupt healthcare services
- There are ethical concerns surrounding the use of AI in medicine, including issues related to patient privacy
Source : https://www.munichre.com/en/company/innovation/tech-trend-radar-2023.item-63b78dc6572d54d4c380b34189b6c273.html?utm_source=google&utm_medium=mrgroupad&utm_campaign=techtrend23&utm_term=apac_generic&utm_content=text&gclid=Cj0KCQjwmN2iBhCrARIsAG_G2i6honVx5MmJB35gDR22nRFyby8lumpdF5EC8Yp60QRynYclp85HgqMaAgVEEALw_wcB