Aakrit Vaish, Co-Founder and CEO, Jio Haptik highlights that AI-enabled intelligent processes and streamlined workflows can make healthcare more affordable, effective, and equitable
The healthcare sector is evolving with enormous challenges aggravated by the increase in lifestyle-related disorders, global population explosion, and waves of pandemic. Patient expectations have shifted to a more streamlined experience, intelligent data analytics, effective payment models, and access to telehealth.
The adoption of telemedicine has skyrocketed by 38X. Based on a McKinsey report, more than 76 per cent of consumers are inclined to use telehealth in the future. Massive consumer data, price transparency mandate, healthcare staffing shortages, and sheer competition drive the industry to technological transformation.
The future of healthcare depends largely on smart approaches. Artificial intelligence is poised to steer the wheel beyond traditional analytics and clinical decision-making. It offers unprecedented insights into diagnostics, care processes, treatment variability, and patient experience, driving improvements across the care continuum.
AI-enabled intelligent processes and streamlined workflows can make healthcare more affordable, effective, and equitable. From handling patient data and records to providing personalised diagnosis and treatment, Artificial intelligence has the potential to propel healthcare through Industry 4.0. With a CAGR of 38.4 per cent from 2022 to 2030 in the global healthcare market, AI and Machine Learning algorithms are being widely adopted to meet surging consumer demands.
Integrating mind and machine through brain-computer interfaces
Neurological disorders and trauma may affect some patients’ abilities to speak, move, and interact coherently with healthcare professionals and their environment. The inability to comprehend patient expressions may lead to inaccurate diagnosis and treatment procedures.
Brain-Computer Interfaces (BCIs) utilise AI and Natural Language Processing (NLP) to integrate the human mind and technology without keyboards, mouse, or monitors. The cutting-edge innovation can decode neural activities and associate them with patients’ expressions to restore their communicative ability. BCIs can drastically improve the quality of life for patients with strokes, ALS, or spinal cord injuries.
Improving the next generation of radiology
Many diagnostic processes still rely on biopsy samples, bypassing non-invasive radio imaging techniques like CT scans, MRIs, and X-rays. It increases the risk of infection and suffers from reduced patient compliance.
Artificial intelligence will facilitate the next-gen radio imaging technologies for a detailed and accurate diagnosis and integrated analysis of patients’ disorders. Surgeons, interventional radiologists, and clinicians may precisely understand critical diseases like cancer and target their treatment regimes more appropriately. AI may also enable “virtual biopsies” that harness image-based algorithms to fuel the innovative field of radionics and characterise the genetic properties of tumours.
Reducing the burdens on health records
Electronic Health Records (EHRs) have successfully handled the growing patient datasets, organised information for better telehealth experiences, and assisted healthcare providers in patient care and administration. However, they encounter obstacles associated with clinical documentation, cognitive overload, and user burnout.
NLP and AI help create more intuitive interfaces to automate routine processes and reduce human effort. Voice recognition and dictation may help improve order entry, while ML-based video indexing may aid clinical information retrieval. AI prioritises tasks that require immediate clinician attention and process routine requests for medicine refills and patient notifications.
Precisely analysing pathology images
More than 70 per cent of all diagnoses and clinical decision-making rely on pathology results. Thus, a faster and more accurate analysis helps drive treatment in the right direction. AI-based analytics can scrutinise sizeable digital pathology images to the pixel-level to identify subtlety that may escape the human eye. It can improve assessment by directing the pathologist’s attention to what’s essential and what is not. It reduces the time they need to spend on each case and increases efficiency.
Bringing smart medical devices to practice
Intelligent devices are taking over the medical environment by monitoring critical patients in the ICU and trauma care. They can integrate diverse data from the healthcare sector, analyse them, and generate an alert for the medical practitioner to adopt a rapid intervention.
Artificial intelligence can make these machines smarter. It enhances the ability to detect deterioration, suggests immediate patient condition, and preempts the complications like the agility with which cancer might progress.
Treating cancer through AI-based immunotherapy
Immunotherapy is a promising avenue in the treatment of cancer using the patient’s intricate immune system to attack tumours and combat metastasis. However, it has an unprecedented response rate and hence lacks adoption.
Offering personalised therapy according to the patient’s unique genetic makeup may alleviate the challenge. Machine learning algorithms help procure massive patient data and analyse complex datasets to identify new treatment options in precision medicine. Oncologists will potentially have a reliable database of which drugs have been beneficial in improving malignancy and grasp a better understanding of the disease biology.
Revolutionising clinical decision-making with real-time monitoring
About 1 in 100,000 people within 35 years of age have sudden deaths every year. Most sudden natural deaths can be attributed to strokes and cardiac arrests, with about 1 in 50,000 deaths of young athletes due to a heart attack. In such cases, predictive analysis and proactive intervention to problems long before diagnosis are crucial to saving lives.
Artificial intelligence at the bedside may provide early warnings of intensive conditions like seizures, sepsis, or cardiac attacks. Also, machine learning can help make decisions on continuing with critical care based on long-term patterns and subtle health improvements.
Final Words
The healthcare industry is ripe with numerous use cases of AI, from patient self-service to chatbots. Computer-Aided Detection (CAD) systems and image data analysis reduce costs and errors, augmenting patient experience with personalised care. Human-like computer applications with advanced integrated technologies like Conversational AI can enhance the overall patient experience. Gathering and tracking patient data, analysing symptoms, scheduling appointments, offering precision medicine, and automating administrative services through conversational AI have made the lives of healthcare professionals easier. Thus, Conversational AI-based platforms with advanced automation, ML, and NLP hold immense potential for improving patient outcomes and driving the healthcare industry to a better future.
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