Research
Artificial Intelligence (AI) in Psychiatry – A Summary
This bibliographic review appraises Artificial Intelligence (AI) theory’s applications for psychiatry. Globally hundreds of millions of people suffer from mental diseases. Hundreds of thousands of people in the world commit suicide and also die from illicit drug overdose due to addiction. Diagnosis and therapy of psychiatric diseases are complex and machine/computer diagnostic tools for physicians are urgently needed to bolster their decision making. This study includes various applications AI/machine learning algorithms in various sub-specialties of psychiatry. AI/ML based psychiatry offers better value over conventional psychiatry in mood disorders, learning disability, children and adolescents mental illnesses, substance abuse. However, numerous implementation challenges for AI in clinical psychiatric practice still remain.
Artificial Intelligence (AI) in Family Medicine – A Summary
This bibliographic review evaluates Artificial Intelligence (AI) theory’s applications in the field of Family Medicine. Globally billions of people suffer from multiple health related issues throughout their lives including diseases of the heart, lungs, kidney, diabetes, and many forms of cancer. Diagnosis, remedy, and prevention of these disorders are multifaceted, and machine/computer based investigative tools for doctors are immediately needed to augment their decision-making. This study includes various applications of AI/machine learning (AI/ML) procedures in family medicine and its various sub-specialties. AI/ML-centered medicine offers better solutions over standard family medicine covering birth through end of life care. These include treatments for adolescents, geriatrics, disorders of pain and sleep, and sports injuries. However, several implementation hurdles for AI in clinical family medicine persist.
Artificial Intelligence (AI) in Pathology – A Summary and Challenges
This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multicenter validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.
