Artificial Intelligence In Medicine: 3 Main Trends And 7 Practical Tips For Startups

If you are an Artificial Intelligence (AI) enthusiast, you must have bumped into reports that researchers are using it to support neural networks and make them reason just like the human brain. Now, that really should come as a surprise because Artificial intelligence is a trending topic in all industries, including medicine.

It has the power to transform healthcare by creating efficiencies, reducing costs, and improving productivity. Some of the most advanced research in artificial intelligence is taking place in the medical industry. With this article, we want you to be aware of the most important facts and trends in the world of artificial intelligence in medicine.

      I.            Telehealth

Telehealth is a real-time connection where patients and providers communicate through virtual means. Telehealth helps people get health care from experts anywhere in the world, even when they cannot leave their homes.

One example of telehealth uses artificial intelligence and machine learning (AI/ML). By using AI/ML, software algorithms can learn how to diagnose symptoms and prescribe medication by analysing data from many other patients who presented with similar symptoms at an earlier date.

A patient’s symptoms will be analysed by the algorithm, which then decides whether it is necessary to visit a doctor or not. The use of telehealth combined with AI makes it possible for people with chronic conditions to participate in self-management of their health.

   II.            Privacy Issues

With the recent advances in AI/ML, many people are concerned about how their personal data is being used to improve services. AI-based diagnostic equipment that can assist doctors with medical imaging or analysis of clinical data results could save time for physicians and hopefully lead to better diagnoses at an earlier stage.

However, there are concerns about whether this personal data should be shared with third-party companies. So it becomes very important not only to implement strict security measures but also to address any privacy concerns around sharing patient information for deep learning purposes (DLP). Providers must work together with legal departments and risk managers to make sure they don’t violate regulatory requirements like HIPAA, which ensures the privacy of individually identifiable health information.

III.            Blockchain

Blockchain provides more security and privacy than traditional databases because data are stored across multiple computers on the blockchain network itself. This decentralized design of blockchain eliminates any single point of failure or hacking attack vulnerability. For AI, this means storing patients’ records in blockchains where its database is distributed throughout the network, so hackers would need to access all nodes at once to corrupt the data held there.

7 Tips for Startups in the Medical Field

With the advancement of AI/ML in medicine, there are many opportunities for medical startups. Here are seven tips for startups to consider when developing products and services in the medical industry:

       I.            Prioritize Security and Privacy

AI is creating new ways by which patient data can be used for diagnosis or other types of treatment across the globe. At the same time, it also brings with it a new concern around security and privacy because large amounts of sensitive health information will flow through these networks. Thus, it is critical that market participants prioritize security and privacy at all stages of their projects so they can protect data against cyber threats while complying with strict data protection legislation like HIPAA in the US.

    II.            Gain FDA approval

The full potential of AI can only be realized if companies gain approval from the Food and Drug Administration (FDA). A significant fraction of patients who use wearable devices or self-monitor their health are not sharing that information with their doctors. With the guidance provided by FDA for medical apps, startups will have an easier time knowing what they need to do to get users’ attention.

 III.            Outline your Goals

Before you start developing any products or services using ML/AI, outline your goals clearly even if that means leaving some of your academic assignments to essay writers, so you don’t waste money on projects that aren’t aligned with your company vision. The same way a business plan outlines key strategic initiatives and milestones for your company, you should create a “go-to-market” plan.

This way, everyone involved in the project understands how it fits into its broader vision. Such a plan can ensure that projects are actually moving forward as planned and give you a clear picture of what you will have achieved by the end of the project, and how will it be executed.

 IV.            Partner with Solution Providers

In an ideal world, AI projects would consist of a single team made up of deep learning/AI experts, machine learning engineers, AI product managers, and AI developers. However, in reality, many companies struggle to find enough qualified staff to successfully deliver deep-learning products or services within their organizations.

Ideally while some startups might have the expertise to build AI/ML models, they may not have the expertise needed to deploy them more broadly across their entire healthcare system. A partner can help address this by providing an orchestration platform that allows users to put together workflows that use existing products or services more easily.

    V.            Ensure Data Quality

There are many companies that tout the value of AI but don’t have the fundamental capabilities to back it up. Rather than investing in technology, they focus on making exaggerated claims about their products and services to win customers and drive growth.

The same is true for startups looking to use ML/AI: Make sure you can deliver on your promises by ensuring data quality is top-notch from the start.

VI.            Embrace the Technology

AI is likely to be a significant part of healthcare in the future, not just for startups but also for large health systems. While many of these organizations are still figuring how to use AI/ML technologies productively, some have already started developing data strategies and creating the necessary infrastructure to support AI initiatives.

VII.            Never Stop Learning

We live in an era when digital healthcare promises to transform how medical professionals provide treatment and improve patient outcomes, but this transformation will take time. As the industry evolves, the AI field will require constant learning by all stakeholders, including entrepreneurs interested in investing or startups looking to build market share with their digital solutions.

Final Words

As AI/ML applications continue to gain momentum in healthcare, it’s important for entrepreneurs to build knowledge in these areas. There are many events and online resources that offer excellent learning opportunities, from small local meetups to larger international conferences that the entrepreneurs can use.

Author Bio:

Charles Normandin is a Goodlett (Texas) based copywriter. He enjoys visiting exhibitions, building new connections and discussing new ways of expressing himself through art. Meet him on Twitter @Charles60104524.