How AI and NLP are revolutionising healthcare services
Utilising AI in Healthcare: Enhancing Diagnostics and Patient Care with NLP and Document Similarity Algorithms
How AI in Healthcare is Transforming Lives of Doctors and Patients
AI in healthcare dramatically improves the efficiency and accuracy of routine tasks in the medical sector.
Particularly in disease diagnosis, AI is proving to be a pivotal development. A report in 2018 highlighted an alarming 40% misdiagnosis rate of cancer patients. As such, improving diagnostic procedures has become a priority and AI has shown promising results in facilitating these improvements. Alleviating the risk of human errors due to high caseloads and incomplete patient histories, AI is facilitating faster and accurate diagnosis, thus helping healthcare professionals deliver better patient care.
For example, a recent study demonstrated an AI model’s prowess at diagnosing breast cancer more accurately than veteran pathologists.
Tracing AI’s Involvement in Healthcare
Artificial intelligence made its foray into the healthcare sector in the 1960s and 70s with the development of Dendral, the original expert system designed for organic chemistry applications. Based on Dendral, the MYCIN system, one of the earliest applications of AI in healthcare, was created.
With the ’80s and ’90s came the advent of microcomputers and increased network connectivity, adding new dimensions to the scope of AI in healthcare. This period fostered the realisation that AI systems could be designed to augment physicians’ expertise while accommodating imperfect data.
Today, advancements in technology and medicine have further ingrained machine learning into the healthcare sector. Improved computer power, growth of genome sequence databases, widespread implementation of electronic health records and developments in natural language processing have facilitated better data processing and collection, thereby bolstering the application of AI in healthcare.
AI in the Current Medical Landscape
AI is becoming more sophisticated with each passing year, opening up limitless possibilities for its application in healthcare, ranging from disease prevention to research, treatment, and diagnosis.
Particularly in medical research, AI is proving to be a game-changer. The journey from research to patient care is long and expensive, often costing more than £1.2 billion per medicine. Given these constraints, it’s easy to understand why AI is gaining importance in streamlining the drug discovery process, significantly reducing the time it takes for new drugs to enter the market.
In the realm of preventive healthcare, AI is empowering individuals to take control of their health by proactively managing their lifestyle. The Internet of Medical Things (IoMT) and AI-powered health apps encourage healthier living and significantly reduce the need for doctor visits.
To learn more about AI applications in healthcare, visit our website.
Pros and Cons of AI in Healthcare
While AI is widely appreciated for its numerous benefits, there are also potential downsides to consider.
AI in healthcare contributes to:
- Improved disease diagnoses
- Better healthcare reach for rural communities
- Early prediction of potential health problems
- Significant time and cost savings
- Advanced surgical assistance
- Augmented abilities and mental health support
However, it also has its cons:
- Reduced personal interaction
- Potential job loss in the healthcare sector
- Risk of defective diagnoses
- Possible social prejudice
For an in-depth discussion on the pros and cons of AI in healthcare, visit Fast Data Science.
