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How To Tackle Healthcare Transformation Powered By Artificial Intelligence (AI)?

It’s obvious for Artificial Intelligence (AI) to bring amazing changed in the healthcare industry and each seems to be better than ever. The real question is about integrating the revolution successfully in our healthcare systems and empower the industry from its core. Doing so requires breaking age old medical and technical chains alongside regulatory complications, dealing with ethical concerns and reduce overselling the technology. The first step to achieve all this is understanding Artificial Intelligence (AI) and its potential in healthcare.

The power of AI in healthcare

Much has been discussed and debated in the potential of AI towards redesigning healthcare, its ability to assist medical professionals in developing treatment blueprints and exploring the best practices for every patient.Besides prioritising emails of doctor and heath tips while keeping them updated, AI also empowers online doctor consultation and healthcare personnel with the most advance and relevant scientific studies associated to the industry.

Some remarkable examples of AI in healthcare can be looked at from several hospitals like;

  • Google DeepMind teamed with the UK’s National Health Service (NHS)for improving the process of delivering the best patient care with digital solutions. DeepMind further expanded its services with the data management app and streams to another hospital in the UK.
  • The Alder Hey Children’s Hospital uses the IBM Watson as part of the science and technology facilities.

Now that we know about the possibilities of AI in healthcare, let’s have a look at obstacles we need to overcome.

Tech limitations of AI

The very term “Artificial Intelligence” at times can be misleading since the associated technology is more advance than where it stands today. At best, the current technology can be eyed as machine learning tactics which expands the Artificial Narrow Intelligence (ANI) in many different fields and its further developing with a remarkable pace.

These intelligent programmes have beaten human counterparts in various tasks like IBM’s Deep Blue, a supercomputer that defeated world champion chess players. Other examples can be of smart algorithms that can drive cars (automatic cars), create amazing artwork and so on.That being said, computers today are capable to interpret videos and images to which we refer as computer vision and the text in frames of natural language processing. Likewise and most remarkable example of the technology in healthcare is of medical imaging.

Medical restrictions

Medical/healthcare restrictions of the present-age can’t be ignored to avoid over-hyping the technology. With image recognition, exploiting machine and deep learning algorithms for radiology, we feed the computers with huge data cluster and then underlying bias. Streamlining and standardising the medical records so that algorithms can correctly interpret them is another limitation of introducing ANI to hospital departments for conducting administrative tasks.

Ethical limitations

While overcoming medical and technological challenges of AI in healthcare is possible, ethical and legal issues are far more difficult. Who’s to blame if the algorithm makes a glitch and unable to detect a cancerous nodule in an X-ray! What if AI predicts something wrongly that can seriously put a valuable patient’s life at stake!

These complex matters must be looked upon with extreme care and precision to achieve the safety and security of AGI. Further, ANI and AGI at a certain point has to be implemented carefully and steadily, allowing appropriate time and space for tapping into potential risks and downsides. Throughout the entire process, independent bioethical research groups alongside medical professionals would closely monitor.

Improved regulations

The very first cloud-based deep learning algorithm for cardiac imaging was developed in 2017 by Arterys which is also approved by the FDA. The regulations surrounding AI however are either lagged behind or currently non-existent. The pace at which this technology’s advancing, we’re likely to see such in hospitals within the coming five and ten years, perhaps a little sooner if key decision makers and high-level policy-makers realise the importance and potential.

Conclusion

These are a few possible challenges and benefits of AI in healthcare with advance-grade doctor and health tips.

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