Artificial intelligence (AI) has transformed our lives by spurring productivity and efficiency. As the digital revolution continues, we are witnessing AI’s impact on every business as the technology simplifies processes, increases productivity and lowers operational costs. AI will be prevalent in healthcare transforming healthcare delivery, disease detection and management, patient care and even public health policy.
Recognizing its capability, healthcare decision-makers, governments, investors and entrepreneurs are focused on leveraging it to eradicate current challenges in public healthcare ecosystems. As such, countries including Finland, Germany, the United Kingdom, Israel, China and the United States have set goals for AI in healthcare by making significant investments in the field. Industry report reveals that funding for the top 50 companies in healthcare-related AI has now reached $8.5bn. AI will be the cornerstone of public healthcare’s transformation, bringing unprecedented progress and enabling millions to live better lives.
Transforming precision medicine through AI-driven diagnostics
One of the most attractive uses of AI in healthcare is its capacity to analyze enormous amounts of medical data quickly and accurately. AI systems can process patient records, diagnostic pictures, genetic information and other pertinent data using machine learning algorithms to help with early detection, diagnosis and individualized treatment programs. The ability to anticipate potential health hazards and customize therapy based on unique patient features thanks to this level of precision is a momentous change because it improves treatment outcomes.
Recent trials by corporations and other healthcare professionals demonstrated the vast potential of AI-driven diagnoses. AI systems trained on massive medical datasets have outperformed human specialists in diagnosing numerous medical illnesses such as cancer, heart disease and neurological disorders. AI-powered diagnostic tools have been demonstrated in studies to drastically reduce diagnostic errors and boost diagnostic speed, resulting in earlier treatments and better patient outcomes. For example, research published in Nature Medicine found that an AI algorithm surpassed doctors in identifying breast cancer from mammograms, giving precision medicine programs a much-needed boost.
Creating a clearer picture
Numerous industry studies have also shown how AI systems can analyze medical images with unmatched precision. AI is positioned to shorten reporting times, maximize resource use and help healthcare professionals make better-educated decisions based on accurate and trustworthy imaging data by supporting radiologists and specialists in their analysis.
Improved preventive care with predictive analytics
AI’s predictive skills are set to shift healthcare from a reactive to a proactive and preventive strategy. AI algorithms can detect patterns that may suggest the risk of specific diseases or health difficulties by analyzing previous patient data, lifestyle factors and environmental changes. With this information, healthcare providers can intervene early, delivering tailored preventative interventions and lifestyle suggestions to reduce risks before they worsen. This change to preventative treatment will not only enhance patient health, but it will also reduce the strain on healthcare institutions and resources.
Expediting drug discovery
The development of a new drug is time-consuming and complicated, which often leads to costly failures. The use of AI can simplify this process by analyzing huge datasets to identify potential drug candidates, predict their efficacy and model side effects. Research into new drugs can be significantly sped up and cost-reduced by using AI-powered simulations, leading to faster access to life-saving medications for patients. Major drug developers recognize AI’s potential to accelerate the analysis of massive databases. This is whilst also saving on costs and promoting better decision making.
AI can also increase treatment options for a range of conditions by repurposing existing drugs for new indications. As an example, our team at Tech Mahindra partnered with ReaGene Biosciences to file a patent for a drug molecule that can potentially attack COVID-19. The choice of strategy was important because the high transmission rate of COVID-19 was partly due to the tight attachment of the virus that facilitates its entry into lung cells.
Our R&D lab, Makers Lab, conducted the molecular docking analysis in-silico. In other words, based on computational docking, machine learning and modeling studies, we were able to fast-track the analysis process and shortlist 17 drug molecules from a list of 8,000 FDA-approved drugs. Several leading life sciences companies further tested these molecules in vitro and finalized them into three molecules that are suitable for use in the industry. Then, the resultant molecules were tested in a 3D bio-printed human vascular lung. This entire experiment was intended to prepare future technology for drug discoveries.
The augmentation of healthcare workforce
AI in public healthcare is not intended to replace healthcare professionals, but to complement and augment them. With AI-driven applications, administration tasks can be handled, patient data can be processed, and EHRs can be managed more efficiently. A study in the Journal of Medical Internet Research suggests that healthcare chatbots powered by artificial intelligence can improve patient engagement and reduce healthcare professionals’ workload by providing accurate information and prompt responses to patient inquiries. Healthcare workers can repurpose their expertise by harnessing AI for more complex, patient-centric care, leading to improved patient outcomes.
As AI’s integration into the public healthcare ecosystem increases, healthcare providers will need to consider data privacy measures. It will be essential to be transparent in explaining AI algorithms to build trust between healthcare professionals and patients. In an era of increasing cyber fraud, healthcare providers such as the NHS will need to ensure patients’ data is secure and private. They should be mandated to comply with stringent regulatory frameworks, such as GDPR and HIPAA, to safeguard sensitive patient information and maintain the highest ethical standards.
Generative AI has produced another shot in the arm for public health care systems. Data within public healthcare is very limited as is understandable because of reluctance to provide data or quality data available. GenAI is now being used to synthesize data to help deep learning models to fit and learn better. The most interesting facet of all of this is how data is governed along with its results. Responsible AI would play a much bigger part in the future of AI-governed healthcare systems.
AI is already transforming public healthcare. Embracing the AI revolution will usher in a new era of healthcare, defined by improved outcomes, enhanced accessibility, and a healthier global population.
