While many current news articles discuss the role of Artificial Intelligence (AI) in consumers’ lives, it’s also transforming global healthcare standards.
From analyzing genetic data to enhancing patient experiences, AI is helping medical facilities provide higher standards of care while powering research.
This article will explore AI’s impact in several areas of healthcare, including the development of personalized medicine, new drugs, and patient treatment plans.
AI’s Role in the Development of New Medications
Developing new drugs wasn’t always a billion-dollar project. Penicillin was discovered by Dr. Alexander Fleming, who returned from vacation to find that a mold called penicillium notatum had contaminated some Petri dishes…and prevented potentially dangerous staphylococcus aureus from growing.
Today’s drug development process can take years and often costs billions. However, AI is beginning to accelerate this process in several ways.
AI Checkers for Healthcare AI Systems
As AI increasingly becomes part of healthcare and medicine, its accuracy and integrity become even more essential. AI checkers provide an effective means for auditing and validating AI systems – especially those used in drug development, personalized medicine, and patient care. AI Checkers evaluate the performance and reliability of artificial intelligence models by cross-referencing their predictions with known data and benchmarks. For instance,
AI Checkers can detect discrepancies in drug interaction predictions and discrepancies or inconsistencies in patient treatment recommendations. They also play a vital role in overseeing AI applications for potential biases and compliance with privacy regulations like HIPAA. By acting as an additional layer of oversight, these AI systems help maintain the trustworthiness of healthcare AI systems, leading to safer patient care overall.
Key AI Applications in Drug Discovery
Here are areas in which AI enables researchers to take their work to the next level.
- Predicting Drug-Target Interactions. AI models screen vast libraries of chemical compounds to identify potential drug candidates that might interact with specific disease targets. Deep Learning Neural networks analyze biological data and help predict molecular interactions.
- New Drug Development. Generative Adversarial Networks (GANs) can generate entirely new molecular structures with desired properties, expanding the universe of potential drug candidates.
AI deep learning models have also expedited research. In 2020, researchers used a deep learning model to identify a novel antibiotic compound effective against a wide range of bacteria, including some antibiotic-resistant strains.
This discovery, made in a fraction of the time traditional methods would have taken, proves AI’s potential when used in research.
Next, you’ll learn how AI can help physicians and medical staff tailor their patient treatment plans.
AI in Personalized Medicine
Creating tailored care that considers each patient’s individual characteristics is ideal. AI is playing a crucial role in making this approach possible.
One example: Support Vector Machines (SVMs) that are used to classify patients into different risk groups or treatment response categories.
Another notable example is IBM’s Watson for Oncology, which analyzes a patient’s medical information against a vast database of medical literature to recommend personalized cancer treatment plans.
Additional AI Applications in Personalized Medicine
Genomic Analysis. AI algorithms, such as random forests, can rapidly analyze an individual’s genetic data to identify potential disease risks and optimal treatment strategies.
Treatment Response Prediction. AI can predict how an individual might respond to specific therapies by analyzing patient data and treatment outcomes. Convolutional Neural Networks (CNNs) can be applied when analyzing medical imaging data for personalized diagnosis and treatment planning.
Precision Dosing. AI models can determine optimal drug dosages based on a patient’s unique characteristics, including genetic makeup, age, weight, and other health factors.
Many of the concepts that enhance personalized medicine also play a role in general patient care, which is our next topic.
AI in Patient Care
AI is actively improving patient care standards, from hospitals to home care to hospice.
Its applications within every facet of care are helping to improve diagnosis and treatment.
Key AI Applications in Patient Care and Treatment
Medical Imaging and Diagnostics. AI algorithms can analyze medical images to detect abnormalities and assist in diagnosis. Convolutional Neural Networks (CNNs) are already widely used in medical image analysis for tasks like tumor detection.
Robotic Surgery. AI-enhanced robots can assist surgeons in performing complex procedures with greater precision.
Predictive Analytics for Patient Monitoring. AI systems can predict patient deterioration or potential complications before they become critical.
Virtual Nursing Assistants. AI-powered chatbots and virtual assistants, similar to in-home models like Alexa and Siri, can provide basic support while monitoring each patient’s physical and emotional status.
Other AI-powered systems that are contributing to patient diagnostics:
- AI-powered Recurrent Neural Networks (RNNs) that analyze time-series data from patient monitoring devices to predict adverse events.
- Google DeepMind, a British-American artificial intelligence research laboratory, has developed an AI-powered retinal scan system that detects over 50 eye diseases. Its accuracy is comparable to that of a trained ophthalmologist.
While the future of AI in healthcare appears to be potentially life-saving, it’s not without its potential drawbacks. Can we satisfactorily address these?
Challenges and Future Directions
These are two areas in which AI algorithms are causing legitimate concerns.
- HIPAA and other privacy laws provide guidelines for protecting the patient data entered into an AI system.
- Addressing possible bias in AI algorithms and the role of AI in medical decision-making is vital.
As AI technology continues to evolve, it has enormous potential to transform the entire healthcare system while improving patient care standards.
