1. AI powered diagnostic tools – are accurate in studying images from CT scans, MRIs and X-rays to improve diagnosis of diseases such as cancer, heart and neurological diseases. These AI tools combined with the expertise of medical professionals will increase the accuracy of disease diagnosis. These tools can identify subtle patterns or anomalies in the scan that medical professionals may miss. Predictive genetics through AI also analyzes patient history and genetic data, helping physicians make better decisions. These instruments are being used extensively by pathologists to study histopathological slides and interpret various manifestations of diseases. 2. Precision medicine – Medicines currently provided to patients are designed based on studies and trials, mostly on a general group of people belonging to a particular geographical area or race. Such one-size-fits-all drugs have many side effects and reduced efficacy. AI is being leveraged to make medicine more effective and accurate. AI helps in understanding the patient's response to the drug and designing the most suitable combination of drugs for that particular patient and as per his/her genetic makeup.
3. Telemedicine – AI-based chatbots and virtual assistants can help improve patient care in underserved areas of the country by providing accurate medical assistance for medication management, symptom monitoring, and initial patient assessment. To promote more inclusive and patient-centred healthcare. AI enabled wearable IoT devices can help in providing accurate data to the medical practitioner. 4. Robotic Assisted Surgery – AI powered surgical robots help surgeons improve their accuracy and skills, as well as reduce recovery time for major and minor surgeries. These bots improve precision and accuracy, resulting in less invasive incisions, thereby reducing recovery time.
While AI in genetics is indeed bringing a positive wave of technological change in healthcare, it also has some drawbacks which are as follows:
1. Data and Privacy – As AI is implemented in healthcare, various levels of patient data will be collected, from medical history to genetic makeup. It is important that this data is protected by the strongest policies with a strong data security infrastructure. This is only possible through transparent implementation of AI and trust between patients and healthcare professionals who understand the ethical implications of the insights produced by AI.
2. Regulatory framework – A strong regulatory framework is needed to govern AI in healthcare. Another challenge that arises is to keep these regulations in sync with the fast-paced development of technology. There is a possibility that regulations may not be able to address new challenges that may emerge with the development of technology. Thus there are concerns about striking a balance between innovation and ethics.
3. Interoperability – Nationwide implementation of AI in healthcare can be a difficult task as healthcare organizations use different systems and data formats, making integration of a common AI across healthcare solutions difficult.
We can easily conclude that AI in healthcare is a complex task and has its own complexities and challenges. Therefore, it is important for technologists, healthcare professionals, policy makers and ethicists to collaborate and improve patient care, disease diagnosis and identification.
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