Examiners will check the results produced by the new AI model. [Photo/Chinanews.com]
Generative artificial intelligence is rapidly providing medical researchers with new treatments, and many new developments are expected across healthcare this year. Most importantly, chemists will devise recipes to create drugs to treat antibiotic resistance, a pressing problem that currently causes millions of deaths each year around the world. A new intelligence model is being used.
Wearable technology AI is also an area that is expected to see new applications in the near future, and in other areas of medical logistics, it is expected that AI will significantly reduce waiting times in hospitals.
To date, six new experimental drugs have been created to combat antibiotic resistance. Using generative AI, the same technology as ChatGPT, the challenge was to find a compound that could treat Acinetobacter baumannii, a bacterium that is one of the leading causes of drug resistance deaths around the world. Researchers have been able to use AI to experiment with multiple computational approaches and develop drugs at unprecedented speed.
The algorithm could be used to scroll through existing drug databases containing more than 100 million known compounds, or even allow researchers to come up with entirely new compounds that were previously unknown.
Once a promising drug was discovered, AI could not only decipher the exact chemical compound of the antibiotic, but also break down the individual steps required to synthesize the antibiotic in the lab.
The chemical space for new drugs is staggering, and all possible drug combinations that physically exist in nature hold untapped and powerful therapeutic effects. Manual human research has only just scratched the surface, and AI is expected to spark an exponential wave of new discoveries in the near future.
The specificity of new antibiotics created along with enhanced analysis of patient biometrics also enables the possibility of personalized medicine, or the best medicine for each individual. This not only increases efficacy but also reduces the risk of side effects for patients.
Consumer wearable technology has been around for several years, but with the advent of AI, it can now be optimized to provide valuable healthcare to patients.
Machine learning can now analyze electrical current activity on the skin to predict when epilepsy patients will have their next seizure. Causal relationships between data points are often overlooked by humans but are discovered by AI. Respiratory rate and composition can also be used to measure metabolism and manage weight.
Heart rate fluctuation trends can also determine if the wearer is fatigued and needs to rest if the body is under stress. These data-driven approaches give users the tools to gain deeper insights into their health, rather than just passive observers unable to make any practical sense from the large amounts of data their bodies provide. .
Scientists are also investigating the use of AI robots to assist medical workers in accident and emergency rooms. Future AI robots may be able to collect patient data such as symptoms and vital signs. This is work that doctors do on a daily basis, and it frees them up to take on more complex tasks, reducing the strain on the system.
Such robots are also expected to be trained to understand a wide range of languages, making it easier to communicate about issues such as consent and symptoms without the need for an interpreter.
However, further research is needed before mainstream deployment. AI is currently unable to pick up on the subtle social cues and behavioral traits that doctors often rely on when evaluating patients. There may also be safety concerns, as a victim of assault or domestic violence may be able to consult a doctor first before reporting to the police.
While these advances in medical AI are promising, it is important to ensure that they are not used skimpily in the delivery of safe and effective healthcare. This is especially true for developing countries. In developing countries, there may be a natural incentive for authorities to rely on automated systems and data-driven recommendations without being checked by trained human doctors.
At the moment, AI has great potential to revolutionize healthcare in general, but it is important to keep in mind its current limitations and ensure that the human eye can monitor its real-world applications.
