AI-driven healthcare: Challenges & Opportunities

About Author: Vinay Phadnis, Co-founder, and CTO, Shubu.ai, has extensive experience in Technologies: Artificial Intelligence, Machine Learning, Blockchain, Quantum Computing, Decentralization, Mathematical Modelling, Data Analysis. 

Digital transformation since the pandemic has been huge. Telehealth has gone from being a novelty to a necessity. Having said that, I wanted to be dependent on health care establishments to induce cures and that we needed technology to do it. An increase in chronic diseases and health care prices, a deficiency of health care professionals, and therefore, resulted in the introduction and usage of AI as a boon in the health care business. AI will create treatments for additional humans. Generally, people believe AI replaces everything. AI in reality can change a number of clinical consultants to seek out longer to pay with their patients.

Some aspects to be focused on by the healthcare industry are, a highly efficient ecosystem to drive efficiency, personalized attention and better outcomes, reduced cost of cure, and reduced burden on healthcare staff.

To achieve all the above, AI needs to be inculcated in the healthcare industry.

Challenges & Opportunities

Some of the challenges faced by the healthcare AI industry are, the huge pressure on healthcare systems and equipment, the exponential growth of healthcare data, producing perfect insights at the point of decision making, augmented intelligence for clinicians, integration and legal challenges, the need for APIs to consume data from the systems, safeguarding patient privacy, interoperability and integration, which are key factors that separate educational analysis from sensible applications of AI.

The opportunities in the healthcare AI industry are:

Speech-to-text AI tool helps docs keep error-free records

Artificial intelligence-powered, real-time speech-to-text applications can help minimize human errors in medical records, and help many doctors in the post-pandemic world to maintain error-free records. The cloud-based software could be a game changer for the medical fraternity. It also addresses burnout issues with the doctor-patient ratio and guarantees error-free documentation from patient to doctor. Most medical errors are because of the lack of clear communication and also the quality of data throughout emergencies. Doctors end up spending a lot of time entering patient data during patient consultations. It addresses the pain point and eases the effort required to input data into EMRs through an intelligent voice-driven user interface. Doctors can produce twice the number of reports in the same time period and power the Indian healthcare space. It is improving turnaround time tremendously. The speech-to-text applications help the doctors with dictating and getting their reports typed, and they also help to edit and finalise the reports faster. The speed and accuracy have helped the doctors to proofread and provide error-free reports. With a cloud-based resolution engineered on highly-advanced and deep-learning models, the application offers the highest accuracy rates out-of-the-box for diverse accents without any voice training. All this is saved in the cloud and available to doctors from any device. It’s like carrying the entire language of medicine with you. As healthcare systems are upgraded to digital and embrace the adoption of electronic medical records, speech-to-text applications will play a critical role.

Voice AI technology allows doctors to focus more on patient care

Conversational AI solutions or voice-based AI technology will convert Electronic Medical Records (EMR) and result in tangible gains in potency and price of documentation across the entire time period of health care for hospitals. The adoption of Voice AI technology has mounted the speed of typewriting by fourfold. It saved 75% of the overall time, i.e., the point of 2 hours spent by doctors on documentation. Voice AI technology permits doctors to focus a lot on patient care and have a major direct impact on very cheap lines for hospitals. For health care systems to be scaled and supported, the necessity of widespread adoption of EMRs is at the forefront of this enablement. It’ll be the default interface for EMR adoption within a short time.

Conclusion

In the healthcare industry, the main concern is the patient. Therefore, doctors have to be compelled to use caution concerning how they’re addressing a number of these AI solutions back at the hospitals and the way a lot of doctors are able to perceive them. Having information systems to manage all the info, having effective information modules and information science techniques also ensure aspects that are quite essential. Deep learning lessons are important to create a number of pictures and illustrations that are decipherable and comprehensible for all information scientists.