AI in Healthcare

Artificial Intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complex medical data. Despite the top player’s claims of advancing at a high rate, AI in healthcare is still at an embryonic stage. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. According to a study by Accenture, total public and private sector investment in healthcare AI is expected to reach $6.6bn in 2021.

Some conditions are required for the development and well-functioning of AI.

People should be aware of the advancement and research that are going on so that they can trust and accept the changes in the medical world. AI enables faster, more efficient development of new drugs, or disease detection years before we even begin to show symptoms.

To do this, communications need to work with a journalistic eye and talk to different teams across the business. IT has to take part in examining the data and figures so that the quality and the extent of data and numbers are significantly more accurate and reliable.

Until and unless we touch people’s lives via Artificial Intelligence in healthcare, people are not going to trust this method of treatment. For this, a unique team of doctors must be created and deliver the best possible results.

IT should understand the risk of inaccurate or incomplete data. All the teams should be working simultaneously to provide 100% accurate information, which is used for further experiments.

They should always be ready with Plan B. In case things go haywire or there is a breach of technology, they should always be prepared with a backup plan and act on it.

With great work comes great responsibility. AI should follow the rules and regulations that are implied by USFDA, CE, and HIPPA. They are responsible for promoting and protecting public health. The companies, too, should be very clear about data privacy intentions, risks, and challenges.

The market impact seems to be life-changing. It can be used to improve predictions of future trends, such as changes in consumer demand, and to manage risk along the supply chain better.

Accenture predicts that the top AI applications may result in annual savings of$150bn by 2026.

The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss. A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.

AI is an advancement in the field of drug science. Patient’s deep and accurate understanding is a help to them. Pattern recognition leads to the detection of disease and drugs, which can cure them; hence, process automation in GTM keeps ongoing.

AI is helping in risk identification. It helps to track identify increased risks, re-admission risk, or relapse. Only after identifying, they can proceed.

Value delivery is one of the main concerns. They are trying to create patient-centric AI for high-quality care, quick TAT, and more accurate products.

Artificial Intelligence makes healthcare systems more productive. Through Digitization, AI products make operations efficient and help in fraud detection.

AI is being used in machine-to-machine engagement and the internet of things, which helps to exchange information without human interference. It also provides virtual nursing assistants.

The remote diagnosis and treatment of patients through telecommunications technology is available at an elementary level for people and predictive diagnostic platform at an advanced level. They are also trying to establish AI-enabled predictive analysis tools to connect the rural and urban healthcare centers via video conferencing, which helps to shorten diagnosis time, provides a solution in an advanced method, and reduces the alarming re-admission rate.

AI in healthcare is the future of patient care. The best opportunity for AI in healthcare over the next few years are hybrid models, where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but retain ultimate responsibility for the patient’s care. With a rapid increase in the list of problems, the market demands innovation in the healthcare industry.

AI-powered solutions are making significant steps for a better health rate. Investments in this area are ramping up by $600m in equity funding in Q2 2018. In the coming years, we see substantial projected equity funding deals and equity deals.

Healthcare centers are adopting the AI method of treatment at an increasing rate to provide better facilities and reduce the rate of relapse or re-admission.

According to statistics and prediction of healthcare weekly, in 2026, robot-assisted surgery, virtual nursing assistants, and administrative workflow assistance are expected to be valued at $40bn, $20bn, and $18bn, respectively.

The author of this article is Swati Sinha (ex-Egon Zehnder/KPMG), a seasoned strategy consultant turned serial entrepreneur. Swati has a proven track record of building businesses from scratch, heading operations in multi city/country environment and working with CXOs as a thought partner.

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