Artificial intelligence (AI), particularly machine learning, has already been applied successfully in healthcare for disease prediction and developing health apps.
However, the successful application ofAIin drug development faces several obstacles, including:
poor model preformance duec to non diverse training data and shortcut learning
the unclear decision making of AI systems which conflicts with regulatory requirements for transparency
some suggestions:
Collaboration Across Disciplines: Propose the idea of fostering collaboration between AI experts, pharmaceutical companies, and regulatory bodies. Such partnerships could enhance the understanding of AI applications and address regulatory challenges more effectively.
Long-Term Monitoring: there should be systems in place for long-term monitoring of AI applications in drug development, not just during the research phase but also after drugs are on the market. This could help in understanding real-world efficacy and safety.
Broader Applications: AI could be applied beyond drug discovery, such as in patient stratification for clinical trials or personalized medicine, to enhance treatment outcomes.
More Investment in AI Technologies: the potential for increased investment in AI technologies within the pharmaceutical industry looks very promising This could help drive innovation and improve efficiency in drug development processes.
THE AUTHER'S OPINION
This study found modest use of AI for drug development focused primarily on early-stage applications and on anticancer and neurological therapies. Possible explanations include a lack of high-quality data available in the subsequent stages of drug discovery and uncertain regulatory expectations concerning late-stage AI applications. Study limitations included relying upon public disclosures by drug manufacturers. Ultimately, this study’s results suggest that greater clarity from medicines regulators is needed to guide sponsors over acceptable AI standards and applications to satisfy marketing authorization requirements
Regulatory considerations
down here we will disply some of the points that we think it has attracted the most attention
Data Quality and Diversity
AI data need to optain certain level of quality which can be achieved by using more and more accurate data in an old trails and experiments and combind them in one big data center.
Validation and Verification
AI aplication must be tested under the real world settings which includes testing its algorithems against independent data sets in many different scenarios
Ethical Considerations
The use of AI in medical applications raises many athecal conserns regarding patient consent, data privacy and algorithems bias we should insure that the algorithems are not bias in any means possible
