Scientists consider that AI will revolutionize our world exactly the way electricity did a century ago. The integration of AI technology in Medical and Healthcare is a major leap forward. Although Healthcare and Medical AI will add extensively to the development and emergence of swift possibilities, it also faces certain challenges.
Integrating Data Sets
Artificial Intelligence and machine learning are undoubtedly considered the highest level of advancement in technology, yet they are dependent on datasets. AI is only effectively operable if it has the necessary datasets. HITECH has been a huge aid in providing structured and raw datasets for machine learning in the healthcare and medical sector. But the challenge lies in the process where the AI learns to combine the data and information extracted from various datasets. It is critically important to consider the budget allocation in this regard as dataset management and maintenance is a pretty costly process. Ultimately, no matter how swift and advanced the healthcare sector has become with the AI assistance, it still has to depend on human touch. The completeness, uniformity, and accuracy of the AI need to be regularly curated.
Data Accessibility and Synthesis
Next, to dataset management, machine learning also faces the challenge of real-time data sharing and accessibility. The healthcare industry requires the accessibility and sharing of case studies and data from around the world to regularly evolve cure and treatment procedures and to conduct research accordingly.
The AI systems in the healthcare industry require a core system where all the patient, payer and provide data is available and shared in real-time worldwide. This is extremely tough to achieve which is why the next best option is to introduce interoperability between the various healthcare AI systems all around the world. Either way, the measurement units is criteria vary from region to region and this would require a highly complex machine system that could share and interoperate patient, medicine and treatment data regularly.
Individuality vs. Mass data
Symptoms, immune system parameters, medicinal effects, and other health factors vary from person to person. This is where the AI systems face the real challenge of appropriate learning and producing effective results. The algorithms of the machines may choose the data extracted from numerous case studies and apply it to an individual who actually possesses different parameters. It is considered that the individual based machine learning and application process is much better than population bases studies.
Though medical AI systems have given us new hopes of profound and comparatively more effective healthcare solutions, they also give rise to new challenges and questions that need to be deal with promptly so that the advancement doesn’t backfire.
Read the source post in Hacker Noon.