If healthcare organizations have to harness the power of big data successfully, they have to accept data-driven decision-making. The use of analytics has to be woven into the organization for developing trust in data. This way, insights can be used for supporting decision-making at an executive level.

To leverage patient data completely, healthcare organizations have to implement non-relational information technology to use data from various sources in various formats.

But it can be difficult to implement these new models and monitor the process of an already-existing system. For instance, new metrics have to be defined for measuring the ROI and performance.

Medical providers have to invest in proper safeguards to protect sensitive patient data.

Several factors contribute to the drug pricing challenge. Almost 20% of the patients request a less expensive alternative when doctors issue prescriptions. The medical community has to come up with a framework to ascertain fair drug pricing. ​

• Identifying Diseases: One of the primary applications of ML is diagnosing and identifying diseases that are otherwise considered difficult to diagnose.
• Personalized Medicine: Personal treatments can be more effective when paired with predictive and health analytics. But it can also be used for better and further disease assessment and research.
• Drug Discovery: Another main application of machine learning and artificial intelligence in healthcare is an early-stage drug discovery process. This includes the Research and Development technologies, such as precision medicine and next-generation sequencing that will help in finding alternative ways for therapy of multifactorial diseases.
• Smart Health Records: Machine learning can ease processes for saving effort, time, and money. Document classification procedures with ML-based OCR recognition and vector machines techniques are eventually gathering steam.
• Outbreak Prediction: Machine learning and artificial intelligence in healthcare technologies are being used to monitor and predict epidemics across the world. Presently, scientists have access to large amounts of data that is being collected from social media channels, satellites, website details, and more for its prediction.

Mad fever has aggravated as fast as the pandemic. Researchers are checking if synthetic intelligence can discover the secrets of COVID-19. Machine learning in this time of need can help in predicting patterns and further outbreaks.

Allied Market Research has predicted that global machine learning medical applications will only increase. According to them, it will reach $22.8 billion by 2023. It can lead to $150 billion annual savings for the healthcare industry.

• Medical data is forbidden for access. As per a survey by Wellcome Foundation survey in the United Kingdom, 17% of public respondents are against sharing their health data with third-party commercial organizations.
• The need for transparent algorithms isn’t only required for catering to strict drug development regulations. However, people need to understand how algorithms generate outcomes.
• The healthcare industry needs to change its view on the importance of data and the way it can add value to long-term perspectives. For instance, pharmaceutical companies are reticent, when it comes to changing their product research and strategies in the absence of instant financial benefits.
• There are a plethora of fragmented details between different databases that require more structuring. When the situation improves, it will culminate in advances to solutions of personal treatment.

• It has to get a better grip of care endpoints. This will help ML achieve its full potential and improve outcomes.
• Identify a defined use case with a requirement for improvement.
• Avoid relying on a single vendor to offer all analytics solutions.
• The healthcare industry has to foster the clinical acceptability of machine learning applications through interactive product development methods.

Artificial intelligence is designed for addressing real-world and specific use cases that make it easier to diagnose, monitor, and treat patients more accurately and efficiently.

The global market can face the challenge to synthesize large volumes of big data through ML methods and techniques, including semantic computing, deep learning, and neural networks.

The prime operational and clinical areas will include medical imaging analytics, clinical decision support, clinical trials, and drug discovery, patient management, and natural language processing.

As per a report by Tractica, Software developers who are looking to address the use cases are more likely to see annual revenue of $8.6 billion by 2025 and contribute $34 billion to software sales, consulting opportunities, and hardware installations in the AI market.

Healthcare organizations expect that machine learning medical applications products will offer actionable insights.