Artificial intelligence or AI uses deep learning to apply cognitive skills through sentiment analysis in developing focused solutions for the healthcare sector. Machine learning is used to develop algorithms that comprehend complex medical data and analyze the results to coordinate clinical methods and patient outcomes.
The use of AI in healthcare is in its nascent stages but the progress has been remarkable which is panning out to diagnostics, treatment protocol, drug development, preventive medicine for critical diseases, and hospital administration. What AI has to offer is more than what has been tapped and utilized for healthcare adherence, as in Industry 5.0 where humans and artificial intelligence collaborate to unlock the full potential of AI for healthcare adherence. The capabilities of AI are vast and largely untapped, presenting a wide range of opportunities for the healthcare industry.
Examples of AI in healthcare
- Diagnostics
Uncertainty is the biggest risk in every sector, and healthcare is not insured against this threat. When repeated tests are not able to decipher the root cause of an ailment, the treatment plan offers a limited scope of improvement in outcomes.
AI is helping the healthcare industry in eliminating the scope of misdiagnosis of patients. Early stage and diagnosis of critical illness through pathology graphic and text analysis of patient pool data with similar genetic composition is predicting the disease stage with accuracy and even suggesting the best course of treatment.
- Pathology
Histology and pathological analysis of cells are considered the gold standard for deep-stage analysis for critical diseases. Numerous diseases like cancer and liver-related diseases refer to biopsy reports. AI-assisted pathology tools search the repositories for similar images and help in diagnosing rare diseases that can be identified which are otherwise difficult to predict and challenging for clinicians to treat without an accurate diagnosis.
- New drug development
Drug development and clinical trials take a lot of time as the data efficiency of humans are slow in comparison to machines. AI solves this problem through semantics and automation that can efficiently scan through large chunks of patient data across different countries. Comparing and identifying the volunteers for clinical trials from healthy people, scanning the results of drug initial stage testing, using different mathematical models to come up with the right combination for the drug, and eliminating human errors that delay the research costs are some of the advantages that AI offers in this aspect.
AI covers neural networks that identify bioactivity patterns from lakhs of data points. Screening large chunks of genetic sequence efficiently with accurate results is possible through neural networking
- Smart medical devices
AI-powered smart medical devices are employed to replace human capabilities in data classification and analysis for major outbreaks and pandemics. Smart vector supports and antimicrobial resistance detection through antigen mapping are efficiently carried out using AI tools that use both robotic and analytical skills required for automation and cognitive recognition.
Apart from predictive diagnostics, AI devices that are designed to recognize the patterns after scanning big data of medical records, optimization of medical treatments, and disease support applications like remote sensors in RPM are increasingly finding their presence in hospital programs.
- Dynamic research
When humans are collecting samples for research and studying the results, the base data is static, and there will be variance between the estimates and the actual results leading to inaccuracies. However, AI uses dynamic components of the data and uses that modeling to pinpoint accurate levels which helps in devising the most effective treatment.
- Virtual assistants
Pre-programmed chatbots with macro answers that cover most of the frequently asked questions and good-to-know information can be passed to the patients through virtual assistants. Interactive responses that are recorded and patient engagement responses that acknowledge the connection and reach out will help the patients be more relaxed as against just sending emails or leaving voice messages without any response for a long time.
Virtual assistants help with setting up appointments with doctors and following up on clinical appointments.
- Hospital administration
AI reduces the work for hospital administration and improves billing with complete adherence to the CPT codes for insurance claims. Complex paperwork that stems from exhaustive insurance players and coverage for different ailments are processed using patient-driven models.
Conclusion:
Automation for scheduling appointments, real-time health status, immediate access to patient history in cases of trauma where there is no reference point, and smart billing solutions are already in use. Various hospitals are relying on robots for minimally invasive surgery thereby increasing the ability to treat more patients. AI in healthcare is the best revolution that we are witnessing.
