The COVID‑19 pandemic has engulfed the world since last year, exhausting the healthcare resources of almost all the countries, causing never‑seen‑before health infrastructure and socioeconomic consequences, particularly in a diverse and billion‑plus country like India.
Similarly, post‑COVID‑19 mental health crisis is inevitable and will be more intriguing and difficult to tackle, which requires face‑to‑face psychiatric consultation for individuals suffering from post-pandemic mental illness and encourage them to seek psychiatric intervention.
Thus, burgeoning world population along with such a catastrophic situation nowadays necessitate adoption of advance technological solutions to create better and quick responding healthcare facilities than ever before to minimize damage in such situations.
Artificial intelligence (AI) is a globally acknowledged technology, which aims to mimic human cognitive functions and play a vital role in human welfare in transparent manner that is compatible with the public interest.
Recent advancements in AI‑based tools, algorithms and machine learning (ML), deep learning, natural language processing, speech recognition, biometric identification, cloud computing, data transmission and smart robots can provide useful insight to understand several aspects of healthcare interventions and can efficiently predict diagnosis and suggest recommendations to thousands of patients, but in less time and prompts judicious use of workforce and resources at a fraction of the cost.
Integration of control system engineering with AI tools such as reinforcement learning, neural network and fuzzy logic has improved the applications of intelligent automatic healthcare systems such as closed-loop drug delivery, life-support systems, image-guided therapy, tailor-made data-driven preventive medicine and robotic surgery.
In addition, complex healthcare issues and wide range of chronic diseases like cardiovascular disease, diabetes, Alzheimer’s, interpreting retinal imaging, detecting arrhythmias, dermatology, echocardiography, neurology screening retinal care, diagnosis process, surgery, identifying genetic facial condition from facial appearance, in vitro fertilization, genomics interpretation and angiography of various types of cancers can be interpreted and solved with improved efficiency of nursing, operational processes and managerial activities by integrating AI and ML tools with a multitude of big data pertaining to health record and human-to-human interactions. This can also provide insights on infection severity and characteristics, patient risk scores and designing and can be shared between decision-makers in a more coordinated and timely manner for adoption of policies during the pandemic.
ML methods can also provide treatment to the whole population using transfer learning to speed-up learning with improved consistency and efficiency to achieve a prescribed level of confidence in findings. AI-enabled systems and IoT can become a cost‑effective alternative to the hospitals for screening individuals suffering from postpandemic mental illness and provide face‑to‑face psychiatric consultation in the postpandemic era.
In addition, AI has gained the attention of consumers, program developers, physicians and researchers or providing training, medical research, diagnosis, clinical examinations, medical treatments, decision making and transformative innovations for public health. AI is utilized to detect digital assaults, cyber-attacks and protect medical services computer systems.
Moreover, using ultra-low power computing, sensing technologies, and network programing, the patients can be remotely monitored, diagnosed, analysed, clinical trials can be expedited and personalized patient medical care plans provided using implantable and wearable medical devices outside of hospitals. It provides user friendly accessibility for both doctor and patients and prepares feedback to medical community for research.
Further, in the current era of fourth Industrial Revolution (4IR), ‘Industry 4.0’ and ‘Smart Factories’ have prompted to invest billions of dollars in AI-based advanced digital technologies and devices with automation and intelligent robots using 5G networking and IoT devices to facilitate error free, high quality R&D by projecting demand driven supply chain management to foster higher margins in stiff competition and to reduce an overwhelming burden in healthcare sector.
However, challenges associated with AI-based healthcare services involve risks of system failures, data-sharing and ownership, human-oriented values, ethical issues, data integrity concerns, cybersecurity and privacy concerns. This necessitates to establish a legal framework for assessment, sharing and utilization of patient’s personal information in a cumulative prospect for public purposes.
Further, social consensus is to develop a platform between industry and the government for big data sharing confidentiality, and liability on medical informatics and during medical accidents or misdiagnosis during the care service and research.
In addition, AI education is required in medical colleges with the increase in utilization of digital devices in industries.
Thus, there is need to analyse strategies for future diagnoses, operational procedures and quality care services, to improve patient treatment efficiency of patient treatment, disease prevention and operational efficiency of hospitals. The integration of AI technologies at local, national, and international levels will improve healthcare systems, save lives and develop swift and efficient methods for implementation.
However, there is always a possibility to improve the cost effectiveness, ease and reach to track complete view of the patient journey on real-time for mass population using apps to cover the spectrum from prevention, state of disease, therapeutic management and therapy-specified outcomes and recommendations for future healthcare.
There is a need to investigate the linkage between service providers, advance digital devices and customers in the healthcare industry.
This will also provide a comprehensive overview of the dynamic healthcare environment developed and current trends of current advancements in technology applications, to analyse the challenges and opportunities will be created during switch over.
The marked significance of the AI inroad into the healthcare sector will tend to accelerate in the future. These AI-led strategic data-driven interventions on health crisis management and medical prevention will provide opportunities to develop actionable insights will bring a paradigm shift to healthcare.
In this digital age, risk management and international healthcare pandemic crisis will foster innovative AI services and big data-derived inferences.